{"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/depth-prediction/papers/ran/1","list_of":"/task/depth-prediction","task":"Depth Prediction","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":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,60],"of":60,"counts":{"archive_papers_tagged":422,"with_a_code_link":203,"where_syntology_ran_a_sample":60,"not_listed_spam_title":0,"listed":422,"listed_where_code_ran":60,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":52,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":52,"listed_every_run_a_failure_of_syntologys_instrument":8,"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/depth-prediction/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/monomvsnet-monocular-priors-guided-multi-view","slug":"monomvsnet-monocular-priors-guided-multi-view","title":"MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network","date":"2025-07-15","arxiv_id":"2507.11333","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"9 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/monomvsnet-monocular-priors-guided-multi-view#ran","syntology_url":"https://syntology.ai/paper/2507.11333","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.11333"}},"official":{"repos":["jianfeij/monomvsnet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/uni4d-unifying-visual-foundation-models-for","slug":"uni4d-unifying-visual-foundation-models-for","title":"Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video","date":"2025-03-27","arxiv_id":"2503.21761","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":1,"phrase":"15 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/uni4d-unifying-visual-foundation-models-for#ran","syntology_url":"https://syntology.ai/paper/2503.21761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.21761"}},"official":{"repos":["Davidyao99/uni4d"],"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/mvsdet-multi-view-indoor-3d-object-detection","slug":"mvsdet-multi-view-indoor-3d-object-detection","title":"MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps","date":"2024-10-28","arxiv_id":"2410.21566","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":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/mvsdet-multi-view-indoor-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2410.21566","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.21566"}},"official":{"repos":["pixie8888/mvsdet"],"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/few-shot-novel-view-synthesis-using-depth","slug":"few-shot-novel-view-synthesis-using-depth","title":"Few-shot Novel View Synthesis using Depth Aware 3D Gaussian Splatting","date":"2024-10-14","arxiv_id":"2410.11080","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-novel-view-synthesis-using-depth#ran","syntology_url":"https://syntology.ai/paper/2410.11080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11080"}},"official":{"repos":["raja-kumar/depth-aware-3dgs"],"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/dreamscene4d-dynamic-multi-object-scene","slug":"dreamscene4d-dynamic-multi-object-scene","title":"DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos","date":"2024-05-03","arxiv_id":"2405.02280","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/dreamscene4d-dynamic-multi-object-scene#ran","syntology_url":"https://syntology.ai/paper/2405.02280","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.02280"}},"official":{"repos":["dreamscene4d/dreamscene4d"],"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/monocd-monocular-3d-object-detection-with","slug":"monocd-monocular-3d-object-detection-with","title":"MonoCD: Monocular 3D Object Detection with Complementary Depths","date":"2024-04-04","arxiv_id":"2404.03181","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 1 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/monocd-monocular-3d-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2404.03181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03181"}},"official":{"repos":["elvintanhust/monocd"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ecodepth-effective-conditioning-of-diffusion","slug":"ecodepth-effective-conditioning-of-diffusion","title":"ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation","date":"2024-03-27","arxiv_id":"2403.18807","repositories_listed":1,"syntology":{"n":15,"n_ran":11,"n_constructed":1,"n_ran_checked":6,"n_instrument":5,"n_unverified":4,"n_honours":2,"n_violates":2,"n_no_contract":2,"n_pointer_only":15,"phrase":"11 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 2 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/ecodepth-effective-conditioning-of-diffusion#ran","syntology_url":"https://syntology.ai/paper/2403.18807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18807"}},"official":{"repos":["aradhye2002/ecodepth"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":1,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/featup-a-model-agnostic-framework-for","slug":"featup-a-model-agnostic-framework-for","title":"FeatUp: A Model-Agnostic Framework for Features at Any Resolution","date":"2024-03-15","arxiv_id":"2403.10516","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/featup-a-model-agnostic-framework-for#ran","syntology_url":"https://syntology.ai/paper/2403.10516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10516"}},"official":{"repos":["mhamilton723/FeatUp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/depth-aware-test-time-training-for-zero-shot","slug":"depth-aware-test-time-training-for-zero-shot","title":"Depth-aware Test-Time Training for Zero-shot Video Object Segmentation","date":"2024-03-07","arxiv_id":"2403.04258","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/depth-aware-test-time-training-for-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2403.04258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.04258"}},"official":{"repos":["NiFangBaAGe/DATTT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/denoising-vision-transformers","slug":"denoising-vision-transformers","title":"Denoising Vision Transformers","date":"2024-01-05","arxiv_id":"2401.02957","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/denoising-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2401.02957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02957"}},"official":{"repos":["Jiawei-Yang/Denoising-ViT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/camera-based-3d-semantic-scene-completion","slug":"camera-based-3d-semantic-scene-completion","title":"Camera-based 3D Semantic Scene Completion with Sparse Guidance Network","date":"2023-12-10","arxiv_id":"2312.05752","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/camera-based-3d-semantic-scene-completion#ran","syntology_url":"https://syntology.ai/paper/2312.05752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05752"}},"official":{"repos":["jieqianyu/sgn"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/stanford-orb-a-real-world-3d-object-inverse","slug":"stanford-orb-a-real-world-3d-object-inverse","title":"Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark","date":"2023-10-24","arxiv_id":"2310.16044","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/stanford-orb-a-real-world-3d-object-inverse#ran","syntology_url":"https://syntology.ai/paper/2310.16044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16044"}},"official":{"repos":["StanfordORB/Stanford-ORB"],"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/constraining-depth-map-geometry-for-multi","slug":"constraining-depth-map-geometry-for-multi","title":"Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells","date":"2023-07-18","arxiv_id":"2307.09160","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/constraining-depth-map-geometry-for-multi#ran","syntology_url":"https://syntology.ai/paper/2307.09160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09160"}},"official":{"repos":["dive128/dmvsnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dualrefine-self-supervised-depth-and-pose","slug":"dualrefine-self-supervised-depth-and-pose","title":"DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium","date":"2023-04-07","arxiv_id":"2304.03560","repositories_listed":2,"syntology":{"n":12,"n_ran":9,"n_constructed":8,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":12,"phrase":"9 ran (of which 8 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) · 3 unverified","sample_list":"/paper/dualrefine-self-supervised-depth-and-pose#ran","syntology_url":"https://syntology.ai/paper/2304.03560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03560"}},"official":{"repos":["antabangun/dualrefine"],"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/nefii-inverse-rendering-for-reflectance","slug":"nefii-inverse-rendering-for-reflectance","title":"NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination","date":"2023-03-29","arxiv_id":"2303.16617","repositories_listed":1,"syntology":{"n":11,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":10,"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) · 10 unverified","sample_list":"/paper/nefii-inverse-rendering-for-reflectance#ran","syntology_url":"https://syntology.ai/paper/2303.16617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16617"}},"official":{"repos":["FuxiComputerVision/Nefii"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/va-depthnet-a-variational-approach-to-single","slug":"va-depthnet-a-variational-approach-to-single","title":"VA-DepthNet: A Variational Approach to Single Image Depth Prediction","date":"2023-02-13","arxiv_id":"2302.06556","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/va-depthnet-a-variational-approach-to-single#ran","syntology_url":"https://syntology.ai/paper/2302.06556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06556"}},"official":{"repos":["cnexah/va-depthnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-from-images-with-a","slug":"self-supervised-learning-from-images-with-a","title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","date":"2023-01-19","arxiv_id":"2301.08243","repositories_listed":7,"syntology":{"n":14,"n_ran":5,"n_constructed":1,"n_ran_checked":3,"n_instrument":2,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":13,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/self-supervised-learning-from-images-with-a#ran","syntology_url":"https://syntology.ai/paper/2301.08243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.08243"}},"official":{"repos":["facebookresearch/ijepa"],"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/aedet-azimuth-invariant-multi-view-3d-object","slug":"aedet-azimuth-invariant-multi-view-3d-object","title":"AeDet: Azimuth-invariant Multi-view 3D Object Detection","date":"2022-11-22","arxiv_id":"2211.12501","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/aedet-azimuth-invariant-multi-view-3d-object#ran","syntology_url":"https://syntology.ai/paper/2211.12501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12501"}},"official":{"repos":["fcjian/AeDet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/croco-self-supervised-pre-training-for-3d","slug":"croco-self-supervised-pre-training-for-3d","title":"CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion","date":"2022-10-19","arxiv_id":"2210.10716","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":7,"n_ran_checked":8,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"12 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/croco-self-supervised-pre-training-for-3d#ran","syntology_url":"https://syntology.ai/paper/2210.10716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10716"}},"official":{"repos":["naver/croco"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/attention-attention-everywhere-monocular","slug":"attention-attention-everywhere-monocular","title":"Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention","date":"2022-10-17","arxiv_id":"2210.09071","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":11,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/attention-attention-everywhere-monocular#ran","syntology_url":"https://syntology.ai/paper/2210.09071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09071"}},"official":{"repos":["ashutosh1807/pixelformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/map-free-visual-relocalization-metric-pose","slug":"map-free-visual-relocalization-metric-pose","title":"Map-free Visual Relocalization: Metric Pose Relative to a Single Image","date":"2022-10-11","arxiv_id":"2210.05494","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"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: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/map-free-visual-relocalization-metric-pose#ran","syntology_url":"https://syntology.ai/paper/2210.05494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05494"}},"official":{"repos":["nianticlabs/map-free-reloc"],"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/self-supervised-monocular-depth-estimation-3","slug":"self-supervised-monocular-depth-estimation-3","title":"Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem","date":"2022-10-02","arxiv_id":"2210.00411","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/self-supervised-monocular-depth-estimation-3#ran","syntology_url":"https://syntology.ai/paper/2210.00411","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00411"}},"official":{"repos":["xingyuuchen/tri-depth"],"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/towards-accurate-reconstruction-of-3d-scene","slug":"towards-accurate-reconstruction-of-3d-scene","title":"Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image","date":"2022-08-28","arxiv_id":"2208.13241","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"10 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/towards-accurate-reconstruction-of-3d-scene#ran","syntology_url":"https://syntology.ai/paper/2208.13241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.13241"}},"official":{"repos":["aim-uofa/depth"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/monovit-self-supervised-monocular-depth","slug":"monovit-self-supervised-monocular-depth","title":"MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer","date":"2022-08-06","arxiv_id":"2208.03543","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"6 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/monovit-self-supervised-monocular-depth#ran","syntology_url":"https://syntology.ai/paper/2208.03543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.03543"}},"official":{"repos":["zxcqlf/monovit"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/depthformer-multiscale-vision-transformer-for","slug":"depthformer-multiscale-vision-transformer-for","title":"Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion","date":"2022-07-10","arxiv_id":"2207.04535","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/depthformer-multiscale-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2207.04535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04535"}},"official":{"repos":["ashutosh1807/depthformer"],"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"]}}},{"url":"/paper/panopticdepth-a-unified-framework-for-depth","slug":"panopticdepth-a-unified-framework-for-depth","title":"PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation","date":"2022-06-01","arxiv_id":"2206.00468","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/panopticdepth-a-unified-framework-for-depth#ran","syntology_url":"https://syntology.ai/paper/2206.00468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00468"}},"official":{"repos":["naiyugao/panopticdepth"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/uvim-a-unified-modeling-approach-for-vision","slug":"uvim-a-unified-modeling-approach-for-vision","title":"UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes","date":"2022-05-20","arxiv_id":"2205.10337","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uvim-a-unified-modeling-approach-for-vision#ran","syntology_url":"https://syntology.ai/paper/2205.10337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10337"}},"official":{"repos":["google-research/big_vision"],"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":["official"]}}},{"url":"/paper/disentangling-object-motion-and-occlusion-for","slug":"disentangling-object-motion-and-occlusion-for","title":"Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth","date":"2022-03-29","arxiv_id":"2203.15174","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/disentangling-object-motion-and-occlusion-for#ran","syntology_url":"https://syntology.ai/paper/2203.15174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15174"}},"official":{"repos":["AutoAILab/DynamicDepth"],"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/new-crfs-neural-window-fully-connected-crfs-1","slug":"new-crfs-neural-window-fully-connected-crfs-1","title":"NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation","date":"2022-03-03","arxiv_id":"2203.01502","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"6 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/new-crfs-neural-window-fully-connected-crfs-1#ran","syntology_url":"https://syntology.ai/paper/2203.01502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01502"}},"official":{"repos":["aliyun/NeWCRFs"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"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/rethinking-depth-estimation-for-multi-view","slug":"rethinking-depth-estimation-for-multi-view","title":"Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation","date":"2022-01-05","arxiv_id":"2201.01501","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rethinking-depth-estimation-for-multi-view#ran","syntology_url":"https://syntology.ai/paper/2201.01501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.01501"}},"official":{"repos":["prstrive/unimvsnet"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["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/advancing-self-supervised-monocular-depth","slug":"advancing-self-supervised-monocular-depth","title":"Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR","date":"2021-09-20","arxiv_id":"2109.09628","repositories_listed":2,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/advancing-self-supervised-monocular-depth#ran","syntology_url":"https://syntology.ai/paper/2109.09628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.09628"}},"official":{"repos":["AutoAILab/FusionDepth","fengziyue/FusionDepth"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"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/nerfactor-neural-factorization-of-shape-and","slug":"nerfactor-neural-factorization-of-shape-and","title":"NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination","date":"2021-06-03","arxiv_id":"2106.01970","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/nerfactor-neural-factorization-of-shape-and#ran","syntology_url":"https://syntology.ai/paper/2106.01970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01970"}},"official":{"repos":["google/nerfactor"],"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/domain-adaptive-semantic-segmentation-with","slug":"domain-adaptive-semantic-segmentation-with","title":"Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation","date":"2021-04-28","arxiv_id":"2104.13613","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/domain-adaptive-semantic-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/2104.13613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.13613"}},"official":{"repos":["qinenergy/corda"],"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/led2-net-monocular-360-layout-estimation-via","slug":"led2-net-monocular-360-layout-estimation-via","title":"LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering","date":"2021-04-01","arxiv_id":"2104.00568","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"10 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/led2-net-monocular-360-layout-estimation-via#ran","syntology_url":"https://syntology.ai/paper/2104.00568","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00568"}},"official":{"repos":["fuenwang/LED2-Net"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/geometric-unsupervised-domain-adaptation-for","slug":"geometric-unsupervised-domain-adaptation-for","title":"Geometric Unsupervised Domain Adaptation for Semantic Segmentation","date":"2021-03-30","arxiv_id":"2103.16694","repositories_listed":0,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/geometric-unsupervised-domain-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2103.16694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16694"}},"official":null}},{"url":"/paper/m3dssd-monocular-3d-single-stage-object","slug":"m3dssd-monocular-3d-single-stage-object","title":"M3DSSD: Monocular 3D Single Stage Object Detector","date":"2021-03-24","arxiv_id":"2103.13164","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/m3dssd-monocular-3d-single-stage-object#ran","syntology_url":"https://syntology.ai/paper/2103.13164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13164"}},"official":{"repos":["mumianyuxin/M3DSSD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/transformers-solve-the-limited-receptive","slug":"transformers-solve-the-limited-receptive","title":"Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction","date":"2021-03-22","arxiv_id":"2103.12091","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":7,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 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; 0 where Syntology's instrument failed) · 4 unverified; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/transformers-solve-the-limited-receptive#ran","syntology_url":"https://syntology.ai/paper/2103.12091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12091"}},"official":{"repos":["ygjwd12345/TransDepth"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/combining-events-and-frames-using-recurrent","slug":"combining-events-and-frames-using-recurrent","title":"Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction","date":"2021-02-18","arxiv_id":"2102.09320","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/combining-events-and-frames-using-recurrent#ran","syntology_url":"https://syntology.ai/paper/2102.09320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.09320"}},"official":{"repos":["uzh-rpg/rpg_ramnet"],"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/unsupervised-monocular-depth-learning-in","slug":"unsupervised-monocular-depth-learning-in","title":"Unsupervised Monocular Depth Learning in Dynamic Scenes","date":"2020-10-30","arxiv_id":"2010.16404","repositories_listed":5,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/unsupervised-monocular-depth-learning-in#ran","syntology_url":"https://syntology.ai/paper/2010.16404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.16404"}},"official":{"repos":["google-research/google-research"],"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/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/non-local-spatial-propagation-network-for","slug":"non-local-spatial-propagation-network-for","title":"Non-Local Spatial Propagation Network for Depth Completion","date":"2020-07-20","arxiv_id":"2007.10042","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":1,"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/non-local-spatial-propagation-network-for#ran","syntology_url":"https://syntology.ai/paper/2007.10042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10042"}},"official":{"repos":["zzangjinsun/NLSPN_ECCV20"],"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/focus-on-defocus-bridging-the-synthetic-to","slug":"focus-on-defocus-bridging-the-synthetic-to","title":"Focus on defocus: bridging the synthetic to real domain gap for depth estimation","date":"2020-05-19","arxiv_id":"2005.09623","repositories_listed":1,"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/focus-on-defocus-bridging-the-synthetic-to#ran","syntology_url":"https://syntology.ai/paper/2005.09623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.09623"}},"official":{"repos":["dvl-tum/defocus-net"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-generalization-joint-depth","slug":"towards-better-generalization-joint-depth","title":"Towards Better Generalization: Joint Depth-Pose Learning without PoseNet","date":"2020-04-03","arxiv_id":"2004.01314","repositories_listed":4,"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/towards-better-generalization-joint-depth#ran","syntology_url":"https://syntology.ai/paper/2004.01314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.01314"}},"official":{"repos":["B1ueber2y/TrianFlow"],"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/semantically-guided-representation-learning-1","slug":"semantically-guided-representation-learning-1","title":"Semantically-Guided Representation Learning for Self-Supervised Monocular Depth","date":"2020-02-27","arxiv_id":"2002.12319","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantically-guided-representation-learning-1#ran","syntology_url":"https://syntology.ai/paper/2002.12319","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.12319"}},"official":{"repos":["TRI-ML/packnet-sfm"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-monocular-depth-hints","slug":"self-supervised-monocular-depth-hints","title":"Self-Supervised Monocular Depth Hints","date":"2019-09-19","arxiv_id":"1909.09051","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/self-supervised-monocular-depth-hints#ran","syntology_url":"https://syntology.ai/paper/1909.09051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.09051"}},"official":{"repos":["nianticlabs/depth-hints"],"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","unlocated"]}}},{"url":"/paper/3d-ken-burns-effect-from-a-single-image","slug":"3d-ken-burns-effect-from-a-single-image","title":"3D Ken Burns Effect from a Single Image","date":"2019-09-12","arxiv_id":"1909.05483","repositories_listed":4,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"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 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) · 1 unverified","sample_list":"/paper/3d-ken-burns-effect-from-a-single-image#ran","syntology_url":"https://syntology.ai/paper/1909.05483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05483"}},"official":{"repos":["sniklaus/3d-ken-burns"],"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/from-big-to-small-multi-scale-local-planar","slug":"from-big-to-small-multi-scale-local-planar","title":"From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation","date":"2019-07-24","arxiv_id":"1907.10326","repositories_listed":14,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/from-big-to-small-multi-scale-local-planar#ran","syntology_url":"https://syntology.ai/paper/1907.10326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10326"}},"official":{"repos":["cogaplex-bts/bts"],"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":["listed","official"]}}},{"url":"/paper/learning-unsupervised-multi-view-stereopsis","slug":"learning-unsupervised-multi-view-stereopsis","title":"Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency","date":"2019-05-07","arxiv_id":"1905.02706","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/learning-unsupervised-multi-view-stereopsis#ran","syntology_url":"https://syntology.ai/paper/1905.02706","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.02706"}},"official":null}},{"url":"/paper/depth-from-videos-in-the-wild-unsupervised","slug":"depth-from-videos-in-the-wild-unsupervised","title":"Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras","date":"2019-04-10","arxiv_id":"1904.04998","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":1,"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/depth-from-videos-in-the-wild-unsupervised#ran","syntology_url":"https://syntology.ai/paper/1904.04998","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04998"}},"official":null}},{"url":"/paper/stereonet-guided-hierarchical-refinement-for","slug":"stereonet-guided-hierarchical-refinement-for","title":"StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction","date":"2018-07-24","arxiv_id":"1807.08865","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"7 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stereonet-guided-hierarchical-refinement-for#ran","syntology_url":"https://syntology.ai/paper/1807.08865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08865"}},"official":null}},{"url":"/paper/competitive-collaboration-joint-unsupervised","slug":"competitive-collaboration-joint-unsupervised","title":"Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation","date":"2018-05-24","arxiv_id":"1805.09806","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"13 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/competitive-collaboration-joint-unsupervised#ran","syntology_url":"https://syntology.ai/paper/1805.09806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09806"}},"official":{"repos":["anuragranj/cc"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/megadepth-learning-single-view-depth","slug":"megadepth-learning-single-view-depth","title":"MegaDepth: Learning Single-View Depth Prediction from Internet Photos","date":"2018-04-02","arxiv_id":"1804.00607","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/megadepth-learning-single-view-depth#ran","syntology_url":"https://syntology.ai/paper/1804.00607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00607"}},"official":null}},{"url":"/paper/sparse-to-dense-depth-prediction-from-sparse","slug":"sparse-to-dense-depth-prediction-from-sparse","title":"Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image","date":"2017-09-21","arxiv_id":"1709.07492","repositories_listed":6,"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/sparse-to-dense-depth-prediction-from-sparse#ran","syntology_url":"https://syntology.ai/paper/1709.07492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.07492"}},"official":{"repos":["fangchangma/sparse-to-dense"],"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/cnn-slam-real-time-dense-monocular-slam-with","slug":"cnn-slam-real-time-dense-monocular-slam-with","title":"CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction","date":"2017-04-11","arxiv_id":"1704.03489","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cnn-slam-real-time-dense-monocular-slam-with#ran","syntology_url":"https://syntology.ai/paper/1704.03489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.03489"}},"official":null}},{"url":"/paper/unsupervised-monocular-depth-estimation-with","slug":"unsupervised-monocular-depth-estimation-with","title":"Unsupervised Monocular Depth Estimation with Left-Right Consistency","date":"2016-09-13","arxiv_id":"1609.03677","repositories_listed":16,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/unsupervised-monocular-depth-estimation-with#ran","syntology_url":"https://syntology.ai/paper/1609.03677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.03677"}},"official":{"repos":["mrharicot/monodepth"],"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/deeper-depth-prediction-with-fully","slug":"deeper-depth-prediction-with-fully","title":"Deeper Depth Prediction with Fully Convolutional Residual Networks","date":"2016-06-01","arxiv_id":"1606.00373","repositories_listed":18,"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/deeper-depth-prediction-with-fully#ran","syntology_url":"https://syntology.ai/paper/1606.00373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.00373"}},"official":{"repos":["iro-cp/FCRN-DepthPrediction"],"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":["listed","official"]}}},{"url":"/paper/predicting-depth-surface-normals-and-semantic","slug":"predicting-depth-surface-normals-and-semantic","title":"Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture","date":"2014-11-18","arxiv_id":"1411.4734","repositories_listed":4,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/predicting-depth-surface-normals-and-semantic#ran","syntology_url":"https://syntology.ai/paper/1411.4734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1411.4734"}},"official":null}}],"record_sha256":"a7c3efea55a8e561dd47e9166562f2cf6a5523c25c9dac097976eb7bc0cd56ca","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}