{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/disparity-loader","entry":"disparity_loader","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":7,"n_papers_ran":0,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":5,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":11,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2209.12699","paper":"/paper/accurate-and-efficient-stereo-matching-via","title":"Accurate and Efficient Stereo Matching via Attention Concatenation Volume","date":"2022-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gangweix/fast-acvnet","path":"datasets/MiddleburyLoader.py","file_url":"https://github.com/gangweix/fast-acvnet/blob/HEAD/datasets/MiddleburyLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f290af6f9436c6b","mcp_get_code":{"code_sha256":"6f290af6f9436c6b"}},{"arxiv_id":"2104.04314","paper":"/paper/cfnet-cascade-and-fused-cost-volume-for","title":"CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching","date":"2021-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gallenszl/MSMD-Net","path":"datasets/MiddleburyLoader.py","file_url":"https://github.com/gallenszl/MSMD-Net/blob/HEAD/datasets/MiddleburyLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f290af6f9436c6b","mcp_get_code":{"code_sha256":"6f290af6f9436c6b"}},{"arxiv_id":"2007.03085","paper":"/paper/wasserstein-distances-for-stereo-disparity","title":"Wasserstein Distances for Stereo Disparity Estimation","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Div99/W-Stereo-Disp","path":"src/disp_dataloader/KITTILoader_dataset3d.py","file_url":"https://github.com/Div99/W-Stereo-Disp/blob/HEAD/src/disp_dataloader/KITTILoader_dataset3d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d9f5f6ce951aa15","mcp_get_code":{"code_sha256":"8d9f5f6ce951aa15"}},{"arxiv_id":"2007.03085","paper":"/paper/wasserstein-distances-for-stereo-disparity","title":"Wasserstein Distances for Stereo Disparity Estimation","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Div99/W-Stereo-Disp","path":"src/disp_dataloader/SceneFlowLoader.py","file_url":"https://github.com/Div99/W-Stereo-Disp/blob/HEAD/src/disp_dataloader/SceneFlowLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aaf877e27c4a3c3e","mcp_get_code":{"code_sha256":"aaf877e27c4a3c3e"}},{"arxiv_id":"1911.04460","paper":"/paper/360sd-net-360-stereo-depth-estimation-with","title":"360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume","date":"2019-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albert100121/360SD-Net","path":"dataloader/RGB_Loader.py","file_url":"https://github.com/albert100121/360SD-Net/blob/HEAD/dataloader/RGB_Loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"65d90cc99b34e88e","mcp_get_code":{"code_sha256":"65d90cc99b34e88e"}},{"arxiv_id":"1906.06310","paper":"/paper/pseudo-lidar-accurate-depth-for-3d-object","title":"Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/Pseudo_Lidar_V2","path":"src/dataloader/KITTILoader_dataset3d.py","file_url":"https://github.com/mileyan/Pseudo_Lidar_V2/blob/HEAD/src/dataloader/KITTILoader_dataset3d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d9f5f6ce951aa15","mcp_get_code":{"code_sha256":"8d9f5f6ce951aa15"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mamoanwar97/Anynet_modified","path":"dataloader/KITTILoader3D.py","file_url":"https://github.com/mamoanwar97/Anynet_modified/blob/HEAD/dataloader/KITTILoader3D.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d9f5f6ce951aa15","mcp_get_code":{"code_sha256":"8d9f5f6ce951aa15"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/AnyNet","path":"dataloader/SecenFlowLoader.py","file_url":"https://github.com/mileyan/AnyNet/blob/HEAD/dataloader/SecenFlowLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aaf877e27c4a3c3e","mcp_get_code":{"code_sha256":"aaf877e27c4a3c3e"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/AnyNet","path":"dataloader/KITTILoader.py","file_url":"https://github.com/mileyan/AnyNet/blob/HEAD/dataloader/KITTILoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c0ce2ceaa41309b3","mcp_get_code":{"code_sha256":"c0ce2ceaa41309b3"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/SecenFlowLoader.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/SecenFlowLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aaf877e27c4a3c3e","mcp_get_code":{"code_sha256":"aaf877e27c4a3c3e"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/KITTILoader.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/KITTILoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c0ce2ceaa41309b3","mcp_get_code":{"code_sha256":"c0ce2ceaa41309b3"}}]}