{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/scene-understanding/papers/ran/2","list_of":"/task/scene-understanding","task":"Scene Understanding","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":208,"counts":{"archive_papers_tagged":1723,"with_a_code_link":720,"where_syntology_ran_a_sample":208,"not_listed_spam_title":0,"listed":1723,"listed_where_code_ran":208,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":182,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":182,"listed_every_run_a_failure_of_syntologys_instrument":26,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/scene-understanding/papers/ran/1","prev":"/task/scene-understanding/papers/ran/1","next":"/task/scene-understanding/papers/ran/3","papers":[{"url":"/paper/towards-in-context-scene-understanding","slug":"towards-in-context-scene-understanding","title":"Towards In-context Scene Understanding","date":"2023-06-02","arxiv_id":"2306.01667","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/towards-in-context-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/2306.01667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01667"}},"official":null}},{"url":"/paper/generating-visual-spatial-description-via","slug":"generating-visual-spatial-description-via","title":"Generating Visual Spatial Description via Holistic 3D Scene Understanding","date":"2023-05-19","arxiv_id":"2305.11768","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/generating-visual-spatial-description-via#ran","syntology_url":"https://syntology.ai/paper/2305.11768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11768"}},"official":{"repos":["zhaoyucs/vsd"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/swin3d-a-pretrained-transformer-backbone-for","slug":"swin3d-a-pretrained-transformer-backbone-for","title":"Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding","date":"2023-04-14","arxiv_id":"2304.06906","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/swin3d-a-pretrained-transformer-backbone-for#ran","syntology_url":"https://syntology.ai/paper/2304.06906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06906"}},"official":{"repos":["microsoft/swin3d"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/idisc-internal-discretization-for-monocular","slug":"idisc-internal-discretization-for-monocular","title":"iDisc: Internal Discretization for Monocular Depth Estimation","date":"2023-04-13","arxiv_id":"2304.06334","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/idisc-internal-discretization-for-monocular#ran","syntology_url":"https://syntology.ai/paper/2304.06334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06334"}},"official":{"repos":["SysCV/idisc"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/topology-reasoning-for-driving-scenes","slug":"topology-reasoning-for-driving-scenes","title":"Graph-based Topology Reasoning for Driving Scenes","date":"2023-04-11","arxiv_id":"2304.05277","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/topology-reasoning-for-driving-scenes#ran","syntology_url":"https://syntology.ai/paper/2304.05277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05277"}},"official":{"repos":["opendrivelab/toponet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fredom-fairness-domain-adaptation-approach-to","slug":"fredom-fairness-domain-adaptation-approach-to","title":"FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding","date":"2023-04-04","arxiv_id":"2304.02135","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/fredom-fairness-domain-adaptation-approach-to#ran","syntology_url":"https://syntology.ai/paper/2304.02135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.02135"}},"official":{"repos":["uark-cviu/fredom"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/regionplc-regional-point-language-contrastive","slug":"regionplc-regional-point-language-contrastive","title":"RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding","date":"2023-04-03","arxiv_id":"2304.00962","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/regionplc-regional-point-language-contrastive#ran","syntology_url":"https://syntology.ai/paper/2304.00962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.00962"}},"official":{"repos":["cvmi-lab/pla"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/complementary-random-masking-for-rgb-thermal","slug":"complementary-random-masking-for-rgb-thermal","title":"Complementary Random Masking for RGB-Thermal Semantic Segmentation","date":"2023-03-30","arxiv_id":"2303.17386","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/complementary-random-masking-for-rgb-thermal#ran","syntology_url":"https://syntology.ai/paper/2303.17386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17386"}},"official":{"repos":["UkcheolShin/CRM_RGBTSeg"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dpf-learning-dense-prediction-fields-with","slug":"dpf-learning-dense-prediction-fields-with","title":"DPF: Learning Dense Prediction Fields with Weak Supervision","date":"2023-03-29","arxiv_id":"2303.16890","repositories_listed":1,"syntology":{"n":34,"n_ran":23,"n_constructed":13,"n_ran_checked":13,"n_instrument":10,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":34,"phrase":"23 ran (of which 13 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 10 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/dpf-learning-dense-prediction-fields-with#ran","syntology_url":"https://syntology.ai/paper/2303.16890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16890"}},"official":{"repos":["cxx226/dpf"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":13,"n_ran_no_instrument_failure":13,"n_unverified":11,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/surroundocc-multi-camera-3d-occupancy","slug":"surroundocc-multi-camera-3d-occupancy","title":"SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving","date":"2023-03-16","arxiv_id":"2303.09551","repositories_listed":2,"syntology":{"n":24,"n_ran":18,"n_constructed":0,"n_ran_checked":8,"n_instrument":10,"n_unverified":6,"n_honours":4,"n_violates":0,"n_no_contract":4,"n_pointer_only":21,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 4 honoured, 0 violated, 4 with no contract checked; 10 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/surroundocc-multi-camera-3d-occupancy#ran","syntology_url":"https://syntology.ai/paper/2303.09551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09551"}},"official":{"repos":["weiyithu/surroundocc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/efficient-computation-sharing-for-multi-task","slug":"efficient-computation-sharing-for-multi-task","title":"Efficient Computation Sharing for Multi-Task Visual Scene Understanding","date":"2023-03-16","arxiv_id":"2303.09663","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/efficient-computation-sharing-for-multi-task#ran","syntology_url":"https://syntology.ai/paper/2303.09663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09663"}},"official":{"repos":["sarashoouri/efficientmtl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pimae-point-cloud-and-image-interactive","slug":"pimae-point-cloud-and-image-interactive","title":"PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection","date":"2023-03-14","arxiv_id":"2303.08129","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/pimae-point-cloud-and-image-interactive#ran","syntology_url":"https://syntology.ai/paper/2303.08129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08129"}},"official":{"repos":["blvlab/pimae"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-neural-embedding-likelihood-for-robust-sim","slug":"3d-neural-embedding-likelihood-for-robust-sim","title":"3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation","date":"2023-02-07","arxiv_id":"2302.03744","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/3d-neural-embedding-likelihood-for-robust-sim#ran","syntology_url":"https://syntology.ai/paper/2302.03744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.03744"}},"official":{"repos":["deepmind/threednel"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/galip-generative-adversarial-clips-for-text","slug":"galip-generative-adversarial-clips-for-text","title":"GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis","date":"2023-01-30","arxiv_id":"2301.12959","repositories_listed":2,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/galip-generative-adversarial-clips-for-text#ran","syntology_url":"https://syntology.ai/paper/2301.12959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.12959"}},"official":{"repos":["tobran/galip"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/unleash-the-potential-of-image-branch-for-1","slug":"unleash-the-potential-of-image-branch-for-1","title":"Unleash the Potential of Image Branch for Cross-modal 3D Object Detection","date":"2023-01-22","arxiv_id":"2301.09077","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/unleash-the-potential-of-image-branch-for-1#ran","syntology_url":"https://syntology.ai/paper/2301.09077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.09077"}},"official":{"repos":["eaphan/upidet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-based-generation-optimization-and","slug":"diffusion-based-generation-optimization-and","title":"Diffusion-based Generation, Optimization, and Planning in 3D Scenes","date":"2023-01-15","arxiv_id":"2301.06015","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":4,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusion-based-generation-optimization-and#ran","syntology_url":"https://syntology.ai/paper/2301.06015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.06015"}},"official":{"repos":["scenediffuser/Scene-Diffuser"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/openscene-3d-scene-understanding-with-open","slug":"openscene-3d-scene-understanding-with-open","title":"OpenScene: 3D Scene Understanding with Open Vocabularies","date":"2022-11-28","arxiv_id":"2211.15654","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/openscene-3d-scene-understanding-with-open#ran","syntology_url":"https://syntology.ai/paper/2211.15654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.15654"}},"official":null}},{"url":"/paper/pareto-manifold-learning-tackling-multiple","slug":"pareto-manifold-learning-tackling-multiple","title":"Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models","date":"2022-10-18","arxiv_id":"2210.09759","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/pareto-manifold-learning-tackling-multiple#ran","syntology_url":"https://syntology.ai/paper/2210.09759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09759"}},"official":{"repos":["nik-dim/pamal"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sqa3d-situated-question-answering-in-3d","slug":"sqa3d-situated-question-answering-in-3d","title":"SQA3D: Situated Question Answering in 3D Scenes","date":"2022-10-14","arxiv_id":"2210.07474","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":6,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sqa3d-situated-question-answering-in-3d#ran","syntology_url":"https://syntology.ai/paper/2210.07474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07474"}},"official":{"repos":["SilongYong/SQA3D"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/image-masking-for-robust-self-supervised","slug":"image-masking-for-robust-self-supervised","title":"Image Masking for Robust Self-Supervised Monocular Depth Estimation","date":"2022-10-05","arxiv_id":"2210.02357","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/image-masking-for-robust-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2210.02357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02357"}},"official":{"repos":["neurai-lab/mimdepth"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/semantic-segmentation-assisted-instance","slug":"semantic-segmentation-assisted-instance","title":"Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation","date":"2022-08-09","arxiv_id":"2208.04766","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semantic-segmentation-assisted-instance#ran","syntology_url":"https://syntology.ai/paper/2208.04766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.04766"}},"official":{"repos":["isunchy/3d_instance_segmentation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/safety-enhanced-autonomous-driving-using-1","slug":"safety-enhanced-autonomous-driving-using-1","title":"Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer","date":"2022-07-28","arxiv_id":"2207.14024","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/safety-enhanced-autonomous-driving-using-1#ran","syntology_url":"https://syntology.ai/paper/2207.14024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14024"}},"official":{"repos":["opendilab/InterFuser"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-abstraction-open-world-3d-scene","slug":"semantic-abstraction-open-world-3d-scene","title":"Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models","date":"2022-07-23","arxiv_id":"2207.11514","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":4,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/semantic-abstraction-open-world-3d-scene#ran","syntology_url":"https://syntology.ai/paper/2207.11514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11514"}},"official":{"repos":["columbia-ai-robotics/semantic-abstraction"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/divide-and-conquer-3d-point-cloud-instance","slug":"divide-and-conquer-3d-point-cloud-instance","title":"Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization","date":"2022-07-22","arxiv_id":"2207.11209","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/divide-and-conquer-3d-point-cloud-instance#ran","syntology_url":"https://syntology.ai/paper/2207.11209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11209"}},"official":{"repos":["weiguangzhao/PBNet"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/slot-order-matters-for-compositional-scene","slug":"slot-order-matters-for-compositional-scene","title":"Towards Improving the Generation Quality of Autoregressive Slot VAEs","date":"2022-06-03","arxiv_id":"2206.01370","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/slot-order-matters-for-compositional-scene#ran","syntology_url":"https://syntology.ai/paper/2206.01370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01370"}},"official":{"repos":["pemami4911/segregate-relate-imagine"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/binsformer-revisiting-adaptive-bins-for","slug":"binsformer-revisiting-adaptive-bins-for","title":"BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation","date":"2022-04-03","arxiv_id":"2204.00987","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/binsformer-revisiting-adaptive-bins-for#ran","syntology_url":"https://syntology.ai/paper/2204.00987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00987"}},"official":{"repos":["zhyever/monocular-depth-estimation-toolbox"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/point-scene-understanding-via-disentangled","slug":"point-scene-understanding-via-disentangled","title":"Point Scene Understanding via Disentangled Instance Mesh Reconstruction","date":"2022-03-31","arxiv_id":"2203.16832","repositories_listed":1,"syntology":{"n":16,"n_ran":16,"n_constructed":0,"n_ran_checked":14,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":2,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/point-scene-understanding-via-disentangled#ran","syntology_url":"https://syntology.ai/paper/2203.16832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16832"}},"official":{"repos":["ashawkey/dimr"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/collaborative-transformers-for-grounded","slug":"collaborative-transformers-for-grounded","title":"Collaborative Transformers for Grounded Situation Recognition","date":"2022-03-30","arxiv_id":"2203.16518","repositories_listed":3,"syntology":{"n":7,"n_ran":5,"n_constructed":3,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/collaborative-transformers-for-grounded#ran","syntology_url":"https://syntology.ai/paper/2203.16518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16518"}},"official":{"repos":["jhcho99/coformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mukea-multimodal-knowledge-extraction-and","slug":"mukea-multimodal-knowledge-extraction-and","title":"MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering","date":"2022-03-17","arxiv_id":"2203.09138","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mukea-multimodal-knowledge-extraction-and#ran","syntology_url":"https://syntology.ai/paper/2203.09138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09138"}},"official":{"repos":["andersonstra/mukea"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weakm3d-towards-weakly-supervised-monocular-1","slug":"weakm3d-towards-weakly-supervised-monocular-1","title":"WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection","date":"2022-03-16","arxiv_id":"2203.08332","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/weakm3d-towards-weakly-supervised-monocular-1#ran","syntology_url":"https://syntology.ai/paper/2203.08332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08332"}},"official":{"repos":["spengliang/weakm3d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/inverted-pyramid-multi-task-transformer-for","slug":"inverted-pyramid-multi-task-transformer-for","title":"InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding","date":"2022-03-15","arxiv_id":"2203.07997","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/inverted-pyramid-multi-task-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2203.07997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07997"}},"official":{"repos":["prismformore/InvPT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic","slug":"cmx-cross-modal-fusion-for-rgb-x-semantic","title":"CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers","date":"2022-03-09","arxiv_id":"2203.04838","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic#ran","syntology_url":"https://syntology.ai/paper/2203.04838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04838"}},"official":{"repos":["huaaaliu/rgbx_semantic_segmentation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/riconv-effective-rotation-invariant","slug":"riconv-effective-rotation-invariant","title":"RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","date":"2022-02-26","arxiv_id":"2202.13094","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/riconv-effective-rotation-invariant#ran","syntology_url":"https://syntology.ai/paper/2202.13094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.13094"}},"official":{"repos":["cszyzhang/riconv2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/transformers-in-self-supervised-monocular","slug":"transformers-in-self-supervised-monocular","title":"Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics","date":"2022-02-07","arxiv_id":"2202.03131","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transformers-in-self-supervised-monocular#ran","syntology_url":"https://syntology.ai/paper/2202.03131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03131"}},"official":null}},{"url":"/paper/global-reasoned-multi-task-learning-model-for","slug":"global-reasoned-multi-task-learning-model-for","title":"Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding","date":"2022-01-28","arxiv_id":"2201.11957","repositories_listed":2,"syntology":{"n":8,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/global-reasoned-multi-task-learning-model-for#ran","syntology_url":"https://syntology.ai/paper/2201.11957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.11957"}},"official":{"repos":["lalithjets/global-reasoned-multi-task-model"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/mseg-a-composite-dataset-for-multi-domain-1","slug":"mseg-a-composite-dataset-for-multi-domain-1","title":"MSeg: A Composite Dataset for Multi-domain Semantic Segmentation","date":"2021-12-27","arxiv_id":"2112.13762","repositories_listed":2,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mseg-a-composite-dataset-for-multi-domain-1#ran","syntology_url":"https://syntology.ai/paper/2112.13762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13762"}},"official":{"repos":["mseg-dataset/mseg-semantic"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/channel-wise-attention-based-network-for-self","slug":"channel-wise-attention-based-network-for-self","title":"Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation","date":"2021-12-24","arxiv_id":"2112.13047","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/channel-wise-attention-based-network-for-self#ran","syntology_url":"https://syntology.ai/paper/2112.13047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13047"}},"official":{"repos":["kamiLight/CADepth-master"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/clevr3d-compositional-language-and-elementary","slug":"clevr3d-compositional-language-and-elementary","title":"Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation","date":"2021-12-22","arxiv_id":"2112.11691","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clevr3d-compositional-language-and-elementary#ran","syntology_url":"https://syntology.ai/paper/2112.11691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.11691"}},"official":{"repos":["yanx27/clevr3d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/behind-the-curtain-learning-occluded-shapes","slug":"behind-the-curtain-learning-occluded-shapes","title":"Behind the Curtain: Learning Occluded Shapes for 3D Object Detection","date":"2021-12-04","arxiv_id":"2112.02205","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/behind-the-curtain-learning-occluded-shapes#ran","syntology_url":"https://syntology.ai/paper/2112.02205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02205"}},"official":{"repos":["xharlie/btcdet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/grounded-situation-recognition-with","slug":"grounded-situation-recognition-with","title":"Grounded Situation Recognition with Transformers","date":"2021-11-19","arxiv_id":"2111.10135","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":9,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/grounded-situation-recognition-with#ran","syntology_url":"https://syntology.ai/paper/2111.10135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.10135"}},"official":{"repos":["jhcho99/gsrtr"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-object-centric-representations-of-1","slug":"learning-object-centric-representations-of-1","title":"Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views","date":"2021-11-13","arxiv_id":"2111.07117","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-object-centric-representations-of-1#ran","syntology_url":"https://syntology.ai/paper/2111.07117","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.07117"}},"official":{"repos":["NanboLi/MulMON"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/planerecnet-multi-task-learning-with-cross","slug":"planerecnet-multi-task-learning-with-cross","title":"PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image","date":"2021-10-21","arxiv_id":"2110.11219","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/planerecnet-multi-task-learning-with-cross#ran","syntology_url":"https://syntology.ai/paper/2110.11219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11219"}},"official":{"repos":["eryixie/planerecnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/structured-bird-s-eye-view-traffic-scene-1","slug":"structured-bird-s-eye-view-traffic-scene-1","title":"Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images","date":"2021-10-05","arxiv_id":"2110.01997","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/structured-bird-s-eye-view-traffic-scene-1#ran","syntology_url":"https://syntology.ai/paper/2110.01997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01997"}},"official":{"repos":["ybarancan/stsu"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/kitti-360-a-novel-dataset-and-benchmarks-for","slug":"kitti-360-a-novel-dataset-and-benchmarks-for","title":"KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D","date":"2021-09-28","arxiv_id":"2109.13410","repositories_listed":2,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/kitti-360-a-novel-dataset-and-benchmarks-for#ran","syntology_url":"https://syntology.ai/paper/2109.13410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.13410"}},"official":{"repos":["autonomousvision/kitti360labeltool","autonomousvision/kitti360scripts"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-segmentation-assisted-scene","slug":"semantic-segmentation-assisted-scene","title":"Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds","date":"2021-09-23","arxiv_id":"2109.11453","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-segmentation-assisted-scene#ran","syntology_url":"https://syntology.ai/paper/2109.11453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.11453"}},"official":{"repos":["jokester-zzz/ssa-sc"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/estimating-and-exploiting-the-aleatoric","slug":"estimating-and-exploiting-the-aleatoric","title":"Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation","date":"2021-09-20","arxiv_id":"2109.09881","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":3,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/estimating-and-exploiting-the-aleatoric#ran","syntology_url":"https://syntology.ai/paper/2109.09881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.09881"}},"official":{"repos":["baegwangbin/surface_normal_uncertainty"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/single-image-3d-object-estimation-with","slug":"single-image-3d-object-estimation-with","title":"Single Image 3D Object Estimation with Primitive Graph Networks","date":"2021-09-09","arxiv_id":"2109.04153","repositories_listed":1,"syntology":{"n":15,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":13,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/single-image-3d-object-estimation-with#ran","syntology_url":"https://syntology.ai/paper/2109.04153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04153"}},"official":{"repos":["hailieqh/3d-object-primitive-graph"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":13,"ran_from_kinds":["official"]}}},{"url":"/paper/spatio-temporal-self-supervised","slug":"spatio-temporal-self-supervised","title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds","date":"2021-09-01","arxiv_id":"2109.00179","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/spatio-temporal-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2109.00179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.00179"}},"official":{"repos":["yichen928/STRL"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/from-general-to-specific-informative-scene","slug":"from-general-to-specific-informative-scene","title":"From General to Specific: Informative Scene Graph Generation via Balance Adjustment","date":"2021-08-30","arxiv_id":"2108.13129","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/from-general-to-specific-informative-scene#ran","syntology_url":"https://syntology.ai/paper/2108.13129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.13129"}},"official":{"repos":["zhugekongkong/sgg-g2s"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-segmentation-in-3d-scenes-using","slug":"instance-segmentation-in-3d-scenes-using","title":"Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks","date":"2021-08-17","arxiv_id":"2108.07478","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/instance-segmentation-in-3d-scenes-using#ran","syntology_url":"https://syntology.ai/paper/2108.07478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.07478"}},"official":{"repos":["gorilla-lab-scut/sstnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-multi-granular-spatio-temporal-graph","slug":"learning-multi-granular-spatio-temporal-graph","title":"Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition","date":"2021-08-10","arxiv_id":"2108.04536","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-multi-granular-spatio-temporal-graph#ran","syntology_url":"https://syntology.ai/paper/2108.04536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04536"}},"official":{"repos":["tailin1009/dualhead-network"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/uninet-a-unified-scene-understanding-network","slug":"uninet-a-unified-scene-understanding-network","title":"UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks","date":"2021-08-10","arxiv_id":"2108.04584","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/uninet-a-unified-scene-understanding-network#ran","syntology_url":"https://syntology.ai/paper/2108.04584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04584"}},"official":{"repos":["NeurAI-Lab/UniNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/neighbor-vote-improving-monocular-3d-object","slug":"neighbor-vote-improving-monocular-3d-object","title":"Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting","date":"2021-07-06","arxiv_id":"2107.02493","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neighbor-vote-improving-monocular-3d-object#ran","syntology_url":"https://syntology.ai/paper/2107.02493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02493"}},"official":null}},{"url":"/paper/part-aware-panoptic-segmentation","slug":"part-aware-panoptic-segmentation","title":"Part-aware Panoptic Segmentation","date":"2021-06-11","arxiv_id":"2106.06351","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/part-aware-panoptic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2106.06351","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06351"}},"official":{"repos":["tue-mps/panoptic_parts"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/light-field-networks-neural-scene","slug":"light-field-networks-neural-scene","title":"Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering","date":"2021-06-04","arxiv_id":"2106.02634","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/light-field-networks-neural-scene#ran","syntology_url":"https://syntology.ai/paper/2106.02634","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02634"}},"official":null}},{"url":"/paper/sspc-net-semi-supervised-semantic-3d-point","slug":"sspc-net-semi-supervised-semantic-3d-point","title":"SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network","date":"2021-04-16","arxiv_id":"2104.07861","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":4,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sspc-net-semi-supervised-semantic-3d-point#ran","syntology_url":"https://syntology.ai/paper/2104.07861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07861"}},"official":{"repos":["MMCheng/SSPC-Net"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-scene-completion-via-integrating","slug":"semantic-scene-completion-via-integrating","title":"Semantic Scene Completion via Integrating Instances and Scene in-the-Loop","date":"2021-04-08","arxiv_id":"2104.03640","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/semantic-scene-completion-via-integrating#ran","syntology_url":"https://syntology.ai/paper/2104.03640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.03640"}},"official":{"repos":["yjcaimeow/SISNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-view-radar-semantic-segmentation","slug":"multi-view-radar-semantic-segmentation","title":"Multi-View Radar Semantic Segmentation","date":"2021-03-30","arxiv_id":"2103.16214","repositories_listed":4,"syntology":{"n":13,"n_ran":9,"n_constructed":7,"n_ran_checked":7,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":13,"phrase":"9 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/multi-view-radar-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2103.16214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16214"}},"official":{"repos":["valeoai/MVRSS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/relation-aware-instance-refinement-for-weakly","slug":"relation-aware-instance-refinement-for-weakly","title":"Relation-aware Instance Refinement for Weakly Supervised Visual Grounding","date":"2021-03-24","arxiv_id":"2103.12989","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/relation-aware-instance-refinement-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2103.12989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12989"}},"official":{"repos":["youngfly11/ReIR-WeaklyGrounding.pytorch"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rellis-3d-dataset-data-benchmarks-and","slug":"rellis-3d-dataset-data-benchmarks-and","title":"RELLIS-3D Dataset: Data, Benchmarks and Analysis","date":"2020-11-17","arxiv_id":"2011.12954","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rellis-3d-dataset-data-benchmarks-and#ran","syntology_url":"https://syntology.ai/paper/2011.12954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12954"}},"official":{"repos":["unmannedlab/RELLIS-3D"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-efficient-scene-understanding-via","slug":"towards-efficient-scene-understanding-via","title":"Towards Efficient Scene Understanding via Squeeze Reasoning","date":"2020-11-06","arxiv_id":"2011.03308","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-efficient-scene-understanding-via#ran","syntology_url":"https://syntology.ai/paper/2011.03308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.03308"}},"official":{"repos":["lxtGH/SFSegNets"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/monocular-depth-estimation-via-listwise","slug":"monocular-depth-estimation-via-listwise","title":"Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model","date":"2020-10-25","arxiv_id":"2010.13118","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/monocular-depth-estimation-via-listwise#ran","syntology_url":"https://syntology.ai/paper/2010.13118","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13118"}},"official":{"repos":["julilien/PLDepth"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/radiate-a-radar-dataset-for-automotive","slug":"radiate-a-radar-dataset-for-automotive","title":"RADIATE: A Radar Dataset for Automotive Perception in Bad Weather","date":"2020-10-18","arxiv_id":"2010.09076","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/radiate-a-radar-dataset-for-automotive#ran","syntology_url":"https://syntology.ai/paper/2010.09076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09076"}},"official":{"repos":["marcelsheeny/radiate_sdk"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/alfworld-aligning-text-and-embodied","slug":"alfworld-aligning-text-and-embodied","title":"ALFWorld: Aligning Text and Embodied Environments for Interactive Learning","date":"2020-10-08","arxiv_id":"2010.03768","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":4,"n_ran_checked":8,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"11 ran (of which 4 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/alfworld-aligning-text-and-embodied#ran","syntology_url":"https://syntology.ai/paper/2010.03768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.03768"}},"official":{"repos":["alfworld/alfworld"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":4,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-semantic-segmentation-of-urban-scale","slug":"towards-semantic-segmentation-of-urban-scale","title":"Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges","date":"2020-09-07","arxiv_id":"2009.03137","repositories_listed":2,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/towards-semantic-segmentation-of-urban-scale#ran","syntology_url":"https://syntology.ai/paper/2009.03137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.03137"}},"official":{"repos":["QingyongHu/SensatUrban"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mlm-a-benchmark-dataset-for-multitask","slug":"mlm-a-benchmark-dataset-for-multitask","title":"MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities","date":"2020-08-14","arxiv_id":"2008.06376","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mlm-a-benchmark-dataset-for-multitask#ran","syntology_url":"https://syntology.ai/paper/2008.06376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06376"}},"official":{"repos":["GOALCLEOPATRA/MLM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/campus3d-a-photogrammetry-point-cloud","slug":"campus3d-a-photogrammetry-point-cloud","title":"Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene","date":"2020-08-11","arxiv_id":"2008.04968","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/campus3d-a-photogrammetry-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2008.04968","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.04968"}},"official":{"repos":["shinke-li/Campus3D"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-3d-object-detection-from-1","slug":"weakly-supervised-3d-object-detection-from-1","title":"Weakly Supervised 3D Object Detection from Point Clouds","date":"2020-07-28","arxiv_id":"2007.13970","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/weakly-supervised-3d-object-detection-from-1#ran","syntology_url":"https://syntology.ai/paper/2007.13970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13970"}},"official":{"repos":["Zengyi-Qin/Weakly-Supervised-3D-Object-Detection"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-object-detection-and-viewpoint","slug":"few-shot-object-detection-and-viewpoint","title":"Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild","date":"2020-07-23","arxiv_id":"2007.12107","repositories_listed":2,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-object-detection-and-viewpoint#ran","syntology_url":"https://syntology.ai/paper/2007.12107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12107"}},"official":null}},{"url":"/paper/pointcontrast-unsupervised-pre-training-for","slug":"pointcontrast-unsupervised-pre-training-for","title":"PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding","date":"2020-07-21","arxiv_id":"2007.10985","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pointcontrast-unsupervised-pre-training-for#ran","syntology_url":"https://syntology.ai/paper/2007.10985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10985"}},"official":{"repos":["facebookresearch/PointContrast"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-and-reasoning-with-the-graph","slug":"learning-and-reasoning-with-the-graph","title":"Learning and Reasoning with the Graph Structure Representation in Robotic Surgery","date":"2020-07-07","arxiv_id":"2007.03357","repositories_listed":2,"syntology":{"n":8,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/learning-and-reasoning-with-the-graph#ran","syntology_url":"https://syntology.ai/paper/2007.03357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03357"}},"official":{"repos":["mobarakol/Surgical_SceneGraph_Generation"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"url":"/paper/learning-visual-commonsense-for-robust-scene","slug":"learning-visual-commonsense-for-robust-scene","title":"Learning Visual Commonsense for Robust Scene Graph Generation","date":"2020-06-17","arxiv_id":"2006.09623","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-visual-commonsense-for-robust-scene#ran","syntology_url":"https://syntology.ai/paper/2006.09623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.09623"}},"official":null}},{"url":"/paper/unmasking-the-inductive-biases-of","slug":"unmasking-the-inductive-biases-of","title":"Benchmarking Unsupervised Object Representations for Video Sequences","date":"2020-06-12","arxiv_id":"2006.07034","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unmasking-the-inductive-biases-of#ran","syntology_url":"https://syntology.ai/paper/2006.07034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07034"}},"official":{"repos":["ecker-lab/object-centric-representation-benchmark"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cityscapes-panoptic-parts-and-pascal-panoptic","slug":"cityscapes-panoptic-parts-and-pascal-panoptic","title":"Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding","date":"2020-04-16","arxiv_id":"2004.07944","repositories_listed":4,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cityscapes-panoptic-parts-and-pascal-panoptic#ran","syntology_url":"https://syntology.ai/paper/2004.07944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07944"}},"official":{"repos":["tue-mps/panoptic_parts"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/self-supervised-scene-de-occlusion","slug":"self-supervised-scene-de-occlusion","title":"Self-Supervised Scene De-occlusion","date":"2020-04-06","arxiv_id":"2004.02788","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-scene-de-occlusion#ran","syntology_url":"https://syntology.ai/paper/2004.02788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.02788"}},"official":null}},{"url":"/paper/pointgroup-dual-set-point-grouping-for-3d","slug":"pointgroup-dual-set-point-grouping-for-3d","title":"PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation","date":"2020-04-03","arxiv_id":"2004.01658","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pointgroup-dual-set-point-grouping-for-3d#ran","syntology_url":"https://syntology.ai/paper/2004.01658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.01658"}},"official":null}},{"url":"/paper/semantic-segmentation-of-underwater-imagery","slug":"semantic-segmentation-of-underwater-imagery","title":"Semantic Segmentation of Underwater Imagery: Dataset and Benchmark","date":"2020-04-02","arxiv_id":"2004.01241","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-segmentation-of-underwater-imagery#ran","syntology_url":"https://syntology.ai/paper/2004.01241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.01241"}},"official":null}},{"url":"/paper/learning-human-object-interaction-detection","slug":"learning-human-object-interaction-detection","title":"Learning Human-Object Interaction Detection using Interaction Points","date":"2020-03-31","arxiv_id":"2003.14023","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-human-object-interaction-detection#ran","syntology_url":"https://syntology.ai/paper/2003.14023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.14023"}},"official":{"repos":["vaesl/IP-Net"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-path-region-mining-for-weakly","slug":"multi-path-region-mining-for-weakly","title":"Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds","date":"2020-03-29","arxiv_id":"2003.13035","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-path-region-mining-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2003.13035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.13035"}},"official":{"repos":["plusmultiply/mprm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/total3dunderstanding-joint-layout-object-pose","slug":"total3dunderstanding-joint-layout-object-pose","title":"Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image","date":"2020-02-27","arxiv_id":"2002.12212","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/total3dunderstanding-joint-layout-object-pose#ran","syntology_url":"https://syntology.ai/paper/2002.12212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.12212"}},"official":{"repos":["yinyunie/Total3DUnderstanding"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pose-aware-multi-level-feature-network-for","slug":"pose-aware-multi-level-feature-network-for","title":"Pose-aware Multi-level Feature Network for Human Object Interaction Detection","date":"2019-09-18","arxiv_id":"1909.08453","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pose-aware-multi-level-feature-network-for#ran","syntology_url":"https://syntology.ai/paper/1909.08453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.08453"}},"official":{"repos":["bobwan1995/PMFNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/global-aggregation-then-local-distribution-in","slug":"global-aggregation-then-local-distribution-in","title":"Global Aggregation then Local Distribution in Fully Convolutional Networks","date":"2019-09-16","arxiv_id":"1909.07229","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/global-aggregation-then-local-distribution-in#ran","syntology_url":"https://syntology.ai/paper/1909.07229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.07229"}},"official":{"repos":["lxtGH/GALD-Net"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rotation-invariant-convolutions-for-3d-point","slug":"rotation-invariant-convolutions-for-3d-point","title":"Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","date":"2019-08-17","arxiv_id":"1908.06297","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/rotation-invariant-convolutions-for-3d-point#ran","syntology_url":"https://syntology.ai/paper/1908.06297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06297"}},"official":{"repos":["hkust-vgd/riconv"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/m3d-rpn-monocular-3d-region-proposal-network","slug":"m3d-rpn-monocular-3d-region-proposal-network","title":"M3D-RPN: Monocular 3D Region Proposal Network for Object Detection","date":"2019-07-13","arxiv_id":"1907.06038","repositories_listed":4,"syntology":{"n":21,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":12,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/m3d-rpn-monocular-3d-region-proposal-network#ran","syntology_url":"https://syntology.ai/paper/1907.06038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.06038"}},"official":{"repos":["garrickbrazil/M3D-RPN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":10,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/part-a2-net-3d-part-aware-and-aggregation","slug":"part-a2-net-3d-part-aware-and-aggregation","title":"From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network","date":"2019-07-08","arxiv_id":"1907.03670","repositories_listed":6,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/part-a2-net-3d-part-aware-and-aggregation#ran","syntology_url":"https://syntology.ai/paper/1907.03670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.03670"}},"official":{"repos":["sshaoshuai/PointCloudDet3D"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/physics-as-inverse-graphics-joint","slug":"physics-as-inverse-graphics-joint","title":"Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video","date":"2019-05-27","arxiv_id":"1905.11169","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/physics-as-inverse-graphics-joint#ran","syntology_url":"https://syntology.ai/paper/1905.11169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.11169"}},"official":null}},{"url":"/paper/a-dataset-for-semantic-segmentation-of-point","slug":"a-dataset-for-semantic-segmentation-of-point","title":"SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences","date":"2019-04-02","arxiv_id":"1904.01416","repositories_listed":5,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-dataset-for-semantic-segmentation-of-point#ran","syntology_url":"https://syntology.ai/paper/1904.01416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01416"}},"official":{"repos":["PRBonn/semantic-kitti-api"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/resunet-a-a-deep-learning-framework-for","slug":"resunet-a-a-deep-learning-framework-for","title":"ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data","date":"2019-04-01","arxiv_id":"1904.00592","repositories_listed":7,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/resunet-a-a-deep-learning-framework-for#ran","syntology_url":"https://syntology.ai/paper/1904.00592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00592"}},"official":{"repos":["Nguyendat-bit/U-net"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/skip-ganomaly-skip-connected-and","slug":"skip-ganomaly-skip-connected-and","title":"Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection","date":"2019-01-25","arxiv_id":"1901.08954","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/skip-ganomaly-skip-connected-and#ran","syntology_url":"https://syntology.ai/paper/1901.08954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08954"}},"official":null}},{"url":"/paper/neural-rgb-d-sensing-depth-and-uncertainty","slug":"neural-rgb-d-sensing-depth-and-uncertainty","title":"Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera","date":"2019-01-09","arxiv_id":"1901.02571","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-rgb-d-sensing-depth-and-uncertainty#ran","syntology_url":"https://syntology.ai/paper/1901.02571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.02571"}},"official":null}},{"url":"/paper/idd-a-dataset-for-exploring-problems-of","slug":"idd-a-dataset-for-exploring-problems-of","title":"IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments","date":"2018-11-26","arxiv_id":"1811.10200","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/idd-a-dataset-for-exploring-problems-of#ran","syntology_url":"https://syntology.ai/paper/1811.10200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10200"}},"official":null}},{"url":"/paper/monogrnet-a-geometric-reasoning-network-for","slug":"monogrnet-a-geometric-reasoning-network-for","title":"MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization","date":"2018-11-26","arxiv_id":"1811.10247","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/monogrnet-a-geometric-reasoning-network-for#ran","syntology_url":"https://syntology.ai/paper/1811.10247","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10247"}},"official":{"repos":["Zengyi-Qin/MonoGRNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cooperative-holistic-scene-understanding","slug":"cooperative-holistic-scene-understanding","title":"Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation","date":"2018-10-31","arxiv_id":"1810.13049","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":1,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cooperative-holistic-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/1810.13049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.13049"}},"official":{"repos":["thusiyuan/cooperative_scene_parsing"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-task-learning-as-multi-objective","slug":"multi-task-learning-as-multi-objective","title":"Multi-Task Learning as Multi-Objective Optimization","date":"2018-10-10","arxiv_id":"1810.04650","repositories_listed":7,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":15,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":1,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-task-learning-as-multi-objective#ran","syntology_url":"https://syntology.ai/paper/1810.04650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.04650"}},"official":{"repos":["IntelVCL/MultiObjectiveOptimization"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/holistic-3d-scene-parsing-and-reconstruction","slug":"holistic-3d-scene-parsing-and-reconstruction","title":"Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image","date":"2018-08-07","arxiv_id":"1808.02201","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/holistic-3d-scene-parsing-and-reconstruction#ran","syntology_url":"https://syntology.ai/paper/1808.02201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.02201"}},"official":null}},{"url":"/paper/unified-perceptual-parsing-for-scene","slug":"unified-perceptual-parsing-for-scene","title":"Unified Perceptual Parsing for Scene Understanding","date":"2018-07-26","arxiv_id":"1807.10221","repositories_listed":25,"syntology":{"n":28,"n_ran":25,"n_constructed":0,"n_ran_checked":18,"n_instrument":7,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":17,"n_pointer_only":10,"phrase":"25 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 1 honoured, 0 violated, 17 with no contract checked; 7 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unified-perceptual-parsing-for-scene#ran","syntology_url":"https://syntology.ai/paper/1807.10221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10221"}},"official":{"repos":["CSAILVision/unifiedparsing"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/digging-into-self-supervised-monocular-depth","slug":"digging-into-self-supervised-monocular-depth","title":"Digging Into Self-Supervised Monocular Depth Estimation","date":"2018-06-04","arxiv_id":"1806.01260","repositories_listed":15,"syntology":{"n":24,"n_ran":23,"n_constructed":0,"n_ran_checked":17,"n_instrument":6,"n_unverified":1,"n_honours":3,"n_violates":1,"n_no_contract":13,"n_pointer_only":10,"phrase":"23 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 3 honoured, 1 violated, 13 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/digging-into-self-supervised-monocular-depth#ran","syntology_url":"https://syntology.ai/paper/1806.01260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.01260"}},"official":{"repos":["nianticlabs/monodepth2"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/multi-resolution-multi-modal-sensor-fusion","slug":"multi-resolution-multi-modal-sensor-fusion","title":"Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty","date":"2018-05-02","arxiv_id":"1805.00930","repositories_listed":4,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-resolution-multi-modal-sensor-fusion#ran","syntology_url":"https://syntology.ai/paper/1805.00930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.00930"}},"official":{"repos":["GatorSense/MIMRF"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/lost-appearance-invariant-place-recognition","slug":"lost-appearance-invariant-place-recognition","title":"LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics","date":"2018-04-16","arxiv_id":"1804.05526","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lost-appearance-invariant-place-recognition#ran","syntology_url":"https://syntology.ai/paper/1804.05526","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05526"}},"official":{"repos":["oravus/lostX"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tensor-comprehensions-framework-agnostic-high","slug":"tensor-comprehensions-framework-agnostic-high","title":"Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions","date":"2018-02-13","arxiv_id":"1802.04730","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tensor-comprehensions-framework-agnostic-high#ran","syntology_url":"https://syntology.ai/paper/1802.04730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.04730"}},"official":null}}],"record_sha256":"aa22b2d41df75ccbaa480140117dd43a4a98b4b863f2f5aaa060fea3221def20","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}