{"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-estimation/papers/ran/3","list_of":"/task/depth-estimation","task":"Depth Estimation","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":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,292],"of":292,"counts":{"archive_papers_tagged":2454,"with_a_code_link":1029,"where_syntology_ran_a_sample":292,"not_listed_spam_title":0,"listed":2454,"listed_where_code_ran":292,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":260,"every_run_a_failure_of_syntologys_instrument":32,"listed_with_a_run_with_no_instrument_failure":260,"listed_every_run_a_failure_of_syntologys_instrument":32,"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-estimation/papers/ran/1","prev":"/task/depth-estimation/papers/ran/2","next":null,"papers":[{"url":"/paper/multi-view-multi-person-3d-pose-estimation","slug":"multi-view-multi-person-3d-pose-estimation","title":"Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo","date":"2021-04-06","arxiv_id":"2104.02273","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":2,"n_ran_checked":3,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/multi-view-multi-person-3d-pose-estimation#ran","syntology_url":"https://syntology.ai/paper/2104.02273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02273"}},"official":{"repos":["jiahaoLjh/PlaneSweepPose"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/objects-are-different-flexible-monocular-3d","slug":"objects-are-different-flexible-monocular-3d","title":"Objects are Different: Flexible Monocular 3D Object Detection","date":"2021-04-06","arxiv_id":"2104.02323","repositories_listed":3,"syntology":{"n":20,"n_ran":14,"n_constructed":1,"n_ran_checked":6,"n_instrument":8,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"14 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 8 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/objects-are-different-flexible-monocular-3d#ran","syntology_url":"https://syntology.ai/paper/2104.02323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02323"}},"official":{"repos":["zhangyp15/MonoFlex"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-two-view-structure-from-motion-revisited","slug":"deep-two-view-structure-from-motion-revisited","title":"Deep Two-View Structure-from-Motion Revisited","date":"2021-04-01","arxiv_id":"2104.00556","repositories_listed":1,"syntology":{"n":21,"n_ran":18,"n_constructed":0,"n_ran_checked":17,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":2,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deep-two-view-structure-from-motion-revisited#ran","syntology_url":"https://syntology.ai/paper/2104.00556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00556"}},"official":{"repos":["jytime/Deep-SfM-Revisited"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":3,"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/adaptive-surface-normal-constraint-for-depth","slug":"adaptive-surface-normal-constraint-for-depth","title":"Adaptive Surface Normal Constraint for Depth Estimation","date":"2021-03-29","arxiv_id":"2103.15483","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/adaptive-surface-normal-constraint-for-depth#ran","syntology_url":"https://syntology.ai/paper/2103.15483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15483"}},"official":{"repos":["xxlong0/ASNDepth"],"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/nemi-unifying-neural-radiance-fields-with","slug":"nemi-unifying-neural-radiance-fields-with","title":"MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis","date":"2021-03-27","arxiv_id":"2103.14910","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/nemi-unifying-neural-radiance-fields-with#ran","syntology_url":"https://syntology.ai/paper/2103.14910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14910"}},"official":{"repos":["vincentfung13/MINE"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"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/vision-transformers-for-dense-prediction","slug":"vision-transformers-for-dense-prediction","title":"Vision Transformers for Dense Prediction","date":"2021-03-24","arxiv_id":"2103.13413","repositories_listed":15,"syntology":{"n":116,"n_ran":65,"n_constructed":19,"n_ran_checked":32,"n_instrument":33,"n_unverified":51,"n_honours":0,"n_violates":0,"n_no_contract":32,"n_pointer_only":15,"phrase":"65 ran (of which 19 constructed an object rather than computing a result; 32 with no instrument failure: 0 honoured, 0 violated, 32 with no contract checked; 33 where Syntology's instrument failed) · 51 unverified","sample_list":"/paper/vision-transformers-for-dense-prediction#ran","syntology_url":"https://syntology.ai/paper/2103.13413","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13413"}},"official":null}},{"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/plade-net-towards-pixel-level-accuracy-for","slug":"plade-net-towards-pixel-level-accuracy-for","title":"PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss","date":"2021-03-12","arxiv_id":"2103.07362","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"4 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/plade-net-towards-pixel-level-accuracy-for#ran","syntology_url":"https://syntology.ai/paper/2103.07362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.07362"}},"official":{"repos":["JuanLuisGonzalez/PLADE-net"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/categorical-depth-distribution-network-for","slug":"categorical-depth-distribution-network-for","title":"Categorical Depth Distribution Network for Monocular 3D Object Detection","date":"2021-03-01","arxiv_id":"2103.01100","repositories_listed":2,"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/categorical-depth-distribution-network-for#ran","syntology_url":"https://syntology.ai/paper/2103.01100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01100"}},"official":{"repos":["TRAILab/CaDDN"],"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/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/hr-depth-high-resolution-self-supervised","slug":"hr-depth-high-resolution-self-supervised","title":"HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation","date":"2020-12-14","arxiv_id":"2012.07356","repositories_listed":1,"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":0,"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/hr-depth-high-resolution-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2012.07356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.07356"}},"official":{"repos":["shawLyu/HR-Depth"],"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/vip-deeplab-learning-visual-perception-with","slug":"vip-deeplab-learning-visual-perception-with","title":"ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation","date":"2020-12-09","arxiv_id":"2012.05258","repositories_listed":2,"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/vip-deeplab-learning-visual-perception-with#ran","syntology_url":"https://syntology.ai/paper/2012.05258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.05258"}},"official":{"repos":["joe-siyuan-qiao/ViP-DeepLab"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"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/what-can-you-learn-from-your-muscles-learning","slug":"what-can-you-learn-from-your-muscles-learning","title":"What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions","date":"2020-10-16","arxiv_id":"2010.08539","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/what-can-you-learn-from-your-muscles-learning#ran","syntology_url":"https://syntology.ai/paper/2010.08539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.08539"}},"official":{"repos":["ehsanik/muscleTorch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spatially-variant-cnn-based-point-spread","slug":"spatially-variant-cnn-based-point-spread","title":"Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy","date":"2020-10-08","arxiv_id":"2010.04011","repositories_listed":1,"syntology":{"n":10,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":9,"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) · 9 unverified","sample_list":"/paper/spatially-variant-cnn-based-point-spread#ran","syntology_url":"https://syntology.ai/paper/2010.04011","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04011"}},"official":{"repos":["idiap/psfestimation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-loss-weighting-with-coefficient-of","slug":"multi-loss-weighting-with-coefficient-of","title":"Multi-Loss Weighting with Coefficient of Variations","date":"2020-09-03","arxiv_id":"2009.01717","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":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) · 0 unverified","sample_list":"/paper/multi-loss-weighting-with-coefficient-of#ran","syntology_url":"https://syntology.ai/paper/2009.01717","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.01717"}},"official":{"repos":["rickgroen/cov-weighting"],"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/visibility-aware-multi-view-stereo-network","slug":"visibility-aware-multi-view-stereo-network","title":"Visibility-aware Multi-view Stereo Network","date":"2020-08-18","arxiv_id":"2008.07928","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/visibility-aware-multi-view-stereo-network#ran","syntology_url":"https://syntology.ai/paper/2008.07928","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.07928"}},"official":{"repos":["jzhangbs/Vis-MVSNet"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/reversing-the-cycle-self-supervised-deep","slug":"reversing-the-cycle-self-supervised-deep","title":"Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation","date":"2020-08-17","arxiv_id":"2008.07130","repositories_listed":1,"syntology":{"n":15,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":10,"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) · 10 unverified","sample_list":"/paper/reversing-the-cycle-self-supervised-deep#ran","syntology_url":"https://syntology.ai/paper/2008.07130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.07130"}},"official":{"repos":["FilippoAleotti/Reversing"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-stereo-from-single-images","slug":"learning-stereo-from-single-images","title":"Learning Stereo from Single Images","date":"2020-08-04","arxiv_id":"2008.01484","repositories_listed":2,"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/learning-stereo-from-single-images#ran","syntology_url":"https://syntology.ai/paper/2008.01484","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.01484"}},"official":{"repos":["nianticlabs/stereo-from-mono"],"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-monocular-3d-face","slug":"self-supervised-monocular-3d-face","title":"Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency","date":"2020-07-24","arxiv_id":"2007.12494","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"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) · 5 unverified","sample_list":"/paper/self-supervised-monocular-3d-face#ran","syntology_url":"https://syntology.ai/paper/2007.12494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12494"}},"official":{"repos":["jiaxiangshang/MGCNet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-person-3d-pose-estimation-in-crowded","slug":"multi-person-3d-pose-estimation-in-crowded","title":"Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry","date":"2020-07-21","arxiv_id":"2007.10986","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/multi-person-3d-pose-estimation-in-crowded#ran","syntology_url":"https://syntology.ai/paper/2007.10986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10986"}},"official":{"repos":["HeCraneChen/3D-Crowd-Pose-Estimation-Based-on-MVG"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":5,"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/p-2-net-patch-match-and-plane-regularization","slug":"p-2-net-patch-match-and-plane-regularization","title":"P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation","date":"2020-07-15","arxiv_id":"2007.07696","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/p-2-net-patch-match-and-plane-regularization#ran","syntology_url":"https://syntology.ai/paper/2007.07696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.07696"}},"official":{"repos":["svip-lab/Indoor-SfMLearner"],"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/continual-adaptation-for-deep-stereo","slug":"continual-adaptation-for-deep-stereo","title":"Continual Adaptation for Deep Stereo","date":"2020-07-10","arxiv_id":"2007.05233","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":8,"n_honours":1,"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; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/continual-adaptation-for-deep-stereo#ran","syntology_url":"https://syntology.ai/paper/2007.05233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05233"}},"official":{"repos":["CVLAB-Unibo/Real-time-self-adaptive-deep-stereo"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/wasserstein-distances-for-stereo-disparity","slug":"wasserstein-distances-for-stereo-disparity","title":"Wasserstein Distances for Stereo Disparity Estimation","date":"2020-07-06","arxiv_id":"2007.03085","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/wasserstein-distances-for-stereo-disparity#ran","syntology_url":"https://syntology.ai/paper/2007.03085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03085"}},"official":{"repos":["Div99/W-Stereo-Disp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/regression-prior-networks","slug":"regression-prior-networks","title":"Regression Prior Networks","date":"2020-06-20","arxiv_id":"2006.11590","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/regression-prior-networks#ran","syntology_url":"https://syntology.ai/paper/2006.11590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11590"}},"official":{"repos":["JanRocketMan/regression-prior-networks"],"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/robust-learning-through-cross-task-1","slug":"robust-learning-through-cross-task-1","title":"Robust Learning Through Cross-Task Consistency","date":"2020-06-07","arxiv_id":"2006.04096","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/robust-learning-through-cross-task-1#ran","syntology_url":"https://syntology.ai/paper/2006.04096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04096"}},"official":{"repos":["EPFL-VILAB/XTConsistency"],"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/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/on-the-uncertainty-of-self-supervised","slug":"on-the-uncertainty-of-self-supervised","title":"On the uncertainty of self-supervised monocular depth estimation","date":"2020-05-13","arxiv_id":"2005.06209","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/on-the-uncertainty-of-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2005.06209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.06209"}},"official":{"repos":["mattpoggi/mono-uncertainty"],"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/consistent-video-depth-estimation","slug":"consistent-video-depth-estimation","title":"Consistent Video Depth Estimation","date":"2020-04-30","arxiv_id":"2004.15021","repositories_listed":3,"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":7,"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/consistent-video-depth-estimation#ran","syntology_url":"https://syntology.ai/paper/2004.15021","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.15021"}},"official":{"repos":["facebookresearch/consistent_depth"],"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/toward-hierarchical-self-supervised-monocular","slug":"toward-hierarchical-self-supervised-monocular","title":"Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications","date":"2020-04-12","arxiv_id":"2004.05560","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/toward-hierarchical-self-supervised-monocular#ran","syntology_url":"https://syntology.ai/paper/2004.05560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05560"}},"official":{"repos":["TJ-IPLab/DNet"],"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/self-supervised-monocular-scene-flow","slug":"self-supervised-monocular-scene-flow","title":"Self-Supervised Monocular Scene Flow Estimation","date":"2020-04-08","arxiv_id":"2004.04143","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/self-supervised-monocular-scene-flow#ran","syntology_url":"https://syntology.ai/paper/2004.04143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.04143"}},"official":{"repos":["visinf/self-mono-sf"],"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/end-to-end-pseudo-lidar-for-image-based-3d","slug":"end-to-end-pseudo-lidar-for-image-based-3d","title":"End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection","date":"2020-04-07","arxiv_id":"2004.03080","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/end-to-end-pseudo-lidar-for-image-based-3d#ran","syntology_url":"https://syntology.ai/paper/2004.03080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03080"}},"official":{"repos":["mileyan/pseudo-LiDAR_e2e"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/guiding-monocular-depth-estimation-using","slug":"guiding-monocular-depth-estimation-using","title":"Guiding Monocular Depth Estimation Using Depth-Attention Volume","date":"2020-04-06","arxiv_id":"2004.02760","repositories_listed":2,"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":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) · 1 unverified","sample_list":"/paper/guiding-monocular-depth-estimation-using#ran","syntology_url":"https://syntology.ai/paper/2004.02760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.02760"}},"official":{"repos":["HuynhLam/DAV"],"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/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/self-supervised-monocular-trained-depth","slug":"self-supervised-monocular-trained-depth","title":"Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume","date":"2020-03-31","arxiv_id":"2003.13951","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-trained-depth#ran","syntology_url":"https://syntology.ai/paper/2003.13951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.13951"}},"official":null}},{"url":"/paper/fast-mvsnet-sparse-to-dense-multi-view-stereo","slug":"fast-mvsnet-sparse-to-dense-multi-view-stereo","title":"Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement","date":"2020-03-29","arxiv_id":"2003.13017","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/fast-mvsnet-sparse-to-dense-multi-view-stereo#ran","syntology_url":"https://syntology.ai/paper/2003.13017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.13017"}},"official":{"repos":["svip-lab/FastMVSNet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/atlas-end-to-end-3d-scene-reconstruction-from","slug":"atlas-end-to-end-3d-scene-reconstruction-from","title":"Atlas: End-to-End 3D Scene Reconstruction from Posed Images","date":"2020-03-23","arxiv_id":"2003.10432","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/atlas-end-to-end-3d-scene-reconstruction-from#ran","syntology_url":"https://syntology.ai/paper/2003.10432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.10432"}},"official":null}},{"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/planar-prior-assisted-patchmatch-multi-view","slug":"planar-prior-assisted-patchmatch-multi-view","title":"Planar Prior Assisted PatchMatch Multi-View Stereo","date":"2019-12-26","arxiv_id":"1912.11744","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/planar-prior-assisted-patchmatch-multi-view#ran","syntology_url":"https://syntology.ai/paper/1912.11744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11744"}},"official":{"repos":["GhiXu/ACMP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-3d-keypoint-learning-for-ego","slug":"self-supervised-3d-keypoint-learning-for-ego","title":"Self-Supervised 3D Keypoint Learning for Ego-motion Estimation","date":"2019-12-07","arxiv_id":"1912.03426","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/self-supervised-3d-keypoint-learning-for-ego#ran","syntology_url":"https://syntology.ai/paper/1912.03426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.03426"}},"official":{"repos":["TRI-ML/KP3D"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/why-having-10000-parameters-in-your-camera","slug":"why-having-10000-parameters-in-your-camera","title":"Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve","date":"2019-12-05","arxiv_id":"1912.02908","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/why-having-10000-parameters-in-your-camera#ran","syntology_url":"https://syntology.ai/paper/1912.02908","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.02908"}},"official":{"repos":["puzzlepaint/camera_calibration"],"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/normal-assisted-stereo-depth-estimation","slug":"normal-assisted-stereo-depth-estimation","title":"Normal Assisted Stereo Depth Estimation","date":"2019-11-24","arxiv_id":"1911.10444","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":2,"phrase":"12 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/normal-assisted-stereo-depth-estimation#ran","syntology_url":"https://syntology.ai/paper/1911.10444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10444"}},"official":{"repos":["udaykusupati/Normal-Assisted-Stereo"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lcd-learned-cross-domain-descriptors-for-2d","slug":"lcd-learned-cross-domain-descriptors-for-2d","title":"LCD: Learned Cross-Domain Descriptors for 2D-3D Matching","date":"2019-11-21","arxiv_id":"1911.09326","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":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) · 0 unverified","sample_list":"/paper/lcd-learned-cross-domain-descriptors-for-2d#ran","syntology_url":"https://syntology.ai/paper/1911.09326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09326"}},"official":{"repos":["hkust-vgd/lcd"],"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/360sd-net-360-stereo-depth-estimation-with","slug":"360sd-net-360-stereo-depth-estimation-with","title":"360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume","date":"2019-11-11","arxiv_id":"1911.04460","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/360sd-net-360-stereo-depth-estimation-with#ran","syntology_url":"https://syntology.ai/paper/1911.04460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.04460"}},"official":null}},{"url":"/paper/cleargrasp-3d-shape-estimation-of-transparent","slug":"cleargrasp-3d-shape-estimation-of-transparent","title":"ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation","date":"2019-10-06","arxiv_id":"1910.02550","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":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) · 5 unverified","sample_list":"/paper/cleargrasp-3d-shape-estimation-of-transparent#ran","syntology_url":"https://syntology.ai/paper/1910.02550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02550"}},"official":{"repos":["Shreeyak/cleargrasp"],"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/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/flow-motion-and-depth-network-for-monocular","slug":"flow-motion-and-depth-network-for-monocular","title":"Flow-Motion and Depth Network for Monocular Stereo and Beyond","date":"2019-09-12","arxiv_id":"1909.05452","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/flow-motion-and-depth-network-for-monocular#ran","syntology_url":"https://syntology.ai/paper/1909.05452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05452"}},"official":{"repos":["HKUST-Aerial-Robotics/Flow-Motion-Depth"],"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/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/unsupervised-scale-consistent-depth-and-ego","slug":"unsupervised-scale-consistent-depth-and-ego","title":"Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video","date":"2019-08-28","arxiv_id":"1908.10553","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unsupervised-scale-consistent-depth-and-ego#ran","syntology_url":"https://syntology.ai/paper/1908.10553","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10553"}},"official":{"repos":["JiawangBian/sc_depth_pl"],"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/a2j-anchor-to-joint-regression-network-for-3d","slug":"a2j-anchor-to-joint-regression-network-for-3d","title":"A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image","date":"2019-08-27","arxiv_id":"1908.09999","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":2,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"8 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; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a2j-anchor-to-joint-regression-network-for-3d#ran","syntology_url":"https://syntology.ai/paper/1908.09999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.09999"}},"official":{"repos":["zhangboshen/A2J"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/indoor-depth-completion-with-boundary","slug":"indoor-depth-completion-with-boundary","title":"Indoor Depth Completion with Boundary Consistency and Self-Attention","date":"2019-08-22","arxiv_id":"1908.08344","repositories_listed":3,"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/indoor-depth-completion-with-boundary#ran","syntology_url":"https://syntology.ai/paper/1908.08344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.08344"}},"official":{"repos":["patrickwu2/Depth-Completion","tsunghan-wu/depth-completion"],"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/exploiting-temporal-consistency-for-real-time","slug":"exploiting-temporal-consistency-for-real-time","title":"Exploiting temporal consistency for real-time video depth estimation","date":"2019-08-10","arxiv_id":"1908.03706","repositories_listed":2,"syntology":{"n":20,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":13,"n_pointer_only":2,"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) · 5 unverified","sample_list":"/paper/exploiting-temporal-consistency-for-real-time#ran","syntology_url":"https://syntology.ai/paper/1908.03706","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.03706"}},"official":null}},{"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/towards-robust-monocular-depth-estimation","slug":"towards-robust-monocular-depth-estimation","title":"Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer","date":"2019-07-02","arxiv_id":"1907.01341","repositories_listed":16,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"13 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/towards-robust-monocular-depth-estimation#ran","syntology_url":"https://syntology.ai/paper/1907.01341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.01341"}},"official":{"repos":["intel-isl/MiDaS"],"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/pseudo-lidar-accurate-depth-for-3d-object","slug":"pseudo-lidar-accurate-depth-for-3d-object","title":"Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving","date":"2019-06-14","arxiv_id":"1906.06310","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":12,"n_pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 1 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pseudo-lidar-accurate-depth-for-3d-object#ran","syntology_url":"https://syntology.ai/paper/1906.06310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.06310"}},"official":{"repos":["mileyan/Pseudo_Lidar_V2"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["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/multi-view-stereo-by-temporal-nonparametric","slug":"multi-view-stereo-by-temporal-nonparametric","title":"Multi-View Stereo by Temporal Nonparametric Fusion","date":"2019-04-12","arxiv_id":"1904.06397","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"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; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-view-stereo-by-temporal-nonparametric#ran","syntology_url":"https://syntology.ai/paper/1904.06397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.06397"}},"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/learning-to-adapt-for-stereo","slug":"learning-to-adapt-for-stereo","title":"Learning to Adapt for Stereo","date":"2019-04-05","arxiv_id":"1904.02957","repositories_listed":1,"syntology":{"n":20,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":10,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/learning-to-adapt-for-stereo#ran","syntology_url":"https://syntology.ai/paper/1904.02957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02957"}},"official":{"repos":["CVLAB-Unibo/Learning2AdaptForStereo"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/structured-knowledge-distillation-for","slug":"structured-knowledge-distillation-for","title":"Structured Knowledge Distillation for Dense Prediction","date":"2019-03-11","arxiv_id":"1903.04197","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"5 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/structured-knowledge-distillation-for#ran","syntology_url":"https://syntology.ai/paper/1903.04197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.04197"}},"official":{"repos":["irfanICMLL/structure_knowledge_distillation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fastdepth-fast-monocular-depth-estimation-on","slug":"fastdepth-fast-monocular-depth-estimation-on","title":"FastDepth: Fast Monocular Depth Estimation on Embedded Systems","date":"2019-03-08","arxiv_id":"1903.03273","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"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 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) · 4 unverified","sample_list":"/paper/fastdepth-fast-monocular-depth-estimation-on#ran","syntology_url":"https://syntology.ai/paper/1903.03273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03273"}},"official":null}},{"url":"/paper/attention-based-context-aggregation-network","slug":"attention-based-context-aggregation-network","title":"Attention-based Context Aggregation Network for Monocular Depth Estimation","date":"2019-01-29","arxiv_id":"1901.10137","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/attention-based-context-aggregation-network#ran","syntology_url":"https://syntology.ai/paper/1901.10137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10137"}},"official":{"repos":["miraiaroha/ACAN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/high-quality-monocular-depth-estimation-via","slug":"high-quality-monocular-depth-estimation-via","title":"High Quality Monocular Depth Estimation via Transfer Learning","date":"2018-12-31","arxiv_id":"1812.11941","repositories_listed":45,"syntology":{"n":23,"n_ran":19,"n_constructed":0,"n_ran_checked":16,"n_instrument":3,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":14,"n_pointer_only":4,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 2 honoured, 0 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/high-quality-monocular-depth-estimation-via#ran","syntology_url":"https://syntology.ai/paper/1812.11941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.11941"}},"official":{"repos":["ialhashim/DenseDepth"],"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/pseudo-lidar-from-visual-depth-estimation","slug":"pseudo-lidar-from-visual-depth-estimation","title":"Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving","date":"2018-12-18","arxiv_id":"1812.07179","repositories_listed":2,"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/pseudo-lidar-from-visual-depth-estimation#ran","syntology_url":"https://syntology.ai/paper/1812.07179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.07179"}},"official":{"repos":["mileyan/pseudo_lidar"],"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/factorized-attention-self-attention-with","slug":"factorized-attention-self-attention-with","title":"Efficient Attention: Attention with Linear Complexities","date":"2018-12-04","arxiv_id":"1812.01243","repositories_listed":14,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":4,"n_no_contract":1,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 4 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/factorized-attention-self-attention-with#ran","syntology_url":"https://syntology.ai/paper/1812.01243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01243"}},"official":{"repos":["cmsflash/efficient-attention"],"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/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/depth-prediction-without-the-sensors","slug":"depth-prediction-without-the-sensors","title":"Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos","date":"2018-11-15","arxiv_id":"1811.06152","repositories_listed":11,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/depth-prediction-without-the-sensors#ran","syntology_url":"https://syntology.ai/paper/1811.06152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.06152"}},"official":null}},{"url":"/paper/bi-real-net-binarizing-deep-network-towards","slug":"bi-real-net-binarizing-deep-network-towards","title":"Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance","date":"2018-11-04","arxiv_id":"1811.01335","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/bi-real-net-binarizing-deep-network-towards#ran","syntology_url":"https://syntology.ai/paper/1811.01335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.01335"}},"official":{"repos":["liuzechun/Bi-Real-net"],"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/anytime-stereo-image-depth-estimation-on","slug":"anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","arxiv_id":"1810.11408","repositories_listed":3,"syntology":{"n":26,"n_ran":20,"n_constructed":0,"n_ran_checked":20,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":2,"n_no_contract":18,"n_pointer_only":2,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 2 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/anytime-stereo-image-depth-estimation-on#ran","syntology_url":"https://syntology.ai/paper/1810.11408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.11408"}},"official":{"repos":["mileyan/AnyNet"],"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":["listed","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/real-time-joint-semantic-segmentation-and","slug":"real-time-joint-semantic-segmentation-and","title":"Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations","date":"2018-09-13","arxiv_id":"1809.04766","repositories_listed":4,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/real-time-joint-semantic-segmentation-and#ran","syntology_url":"https://syntology.ai/paper/1809.04766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.04766"}},"official":{"repos":["DrSleep/multi-task-refinenet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"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/mvdepthnet-real-time-multiview-depth","slug":"mvdepthnet-real-time-multiview-depth","title":"MVDepthNet: Real-time Multiview Depth Estimation Neural Network","date":"2018-07-23","arxiv_id":"1807.08563","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/mvdepthnet-real-time-multiview-depth#ran","syntology_url":"https://syntology.ai/paper/1807.08563","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08563"}},"official":null}},{"url":"/paper/deep-ordinal-regression-network-for-monocular","slug":"deep-ordinal-regression-network-for-monocular","title":"Deep Ordinal Regression Network for Monocular Depth Estimation","date":"2018-06-06","arxiv_id":"1806.02446","repositories_listed":5,"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/deep-ordinal-regression-network-for-monocular#ran","syntology_url":"https://syntology.ai/paper/1806.02446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02446"}},"official":{"repos":["hufu6371/DORN"],"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/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/pyramid-stereo-matching-network","slug":"pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","arxiv_id":"1803.08669","repositories_listed":6,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pyramid-stereo-matching-network#ran","syntology_url":"https://syntology.ai/paper/1803.08669","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08669"}},"official":{"repos":["JiaRenChang/PSMNet"],"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/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/what-uncertainties-do-we-need-in-bayesian","slug":"what-uncertainties-do-we-need-in-bayesian","title":"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?","date":"2017-03-15","arxiv_id":"1703.04977","repositories_listed":11,"syntology":{"n":5,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/what-uncertainties-do-we-need-in-bayesian#ran","syntology_url":"https://syntology.ai/paper/1703.04977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.04977"}},"official":null}},{"url":"/paper/a-general-and-adaptive-robust-loss-function","slug":"a-general-and-adaptive-robust-loss-function","title":"A General and Adaptive Robust Loss Function","date":"2017-01-11","arxiv_id":"1701.03077","repositories_listed":3,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"11 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-general-and-adaptive-robust-loss-function#ran","syntology_url":"https://syntology.ai/paper/1701.03077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.03077"}},"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/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/single-image-depth-perception-in-the-wild","slug":"single-image-depth-perception-in-the-wild","title":"Single-Image Depth Perception in the Wild","date":"2016-04-13","arxiv_id":"1604.03901","repositories_listed":4,"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":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) · 0 unverified","sample_list":"/paper/single-image-depth-perception-in-the-wild#ran","syntology_url":"https://syntology.ai/paper/1604.03901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.03901"}},"official":null}},{"url":"/paper/unsupervised-cnn-for-single-view-depth","slug":"unsupervised-cnn-for-single-view-depth","title":"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue","date":"2016-03-16","arxiv_id":"1603.04992","repositories_listed":2,"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/unsupervised-cnn-for-single-view-depth#ran","syntology_url":"https://syntology.ai/paper/1603.04992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.04992"}},"official":{"repos":["Ravi-Garg/Unsupervised_Depth_Estimation"],"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":["listed","unlocated"]}}},{"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":"b5f615e67a6368547ab1929fd21842c5175d4592b422212a6a271d123f0ec5da","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}