{"url":"/task/stereo-disparity-estimation","name":"Stereo Disparity Estimation","slug":"stereo-disparity-estimation","description_markdown":null,"categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":29,"papers_with_code":19,"benchmarks":3,"benchmark_tables_in_archive":3,"benchmark_tables_shown":3,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":7,"subtasks":1,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/stereo-disparity-estimation-on-scene-flow","slug":"stereo-disparity-estimation-on-scene-flow","dataset":"Scene Flow","dataset_url":null,"rows_in_archive":7,"metrics":["EPE","one pixel error","three pixel error"],"first_row_in_archive_order":{"model":"AANet","paper_title":"AANet: Adaptive Aggregation Network for Efficient Stereo Matching","paper_url":"/paper/aanet-adaptive-aggregation-network-for","paper_date":"2020-04-20","arxiv_id":"2004.09548","code_links":[{"title":"haofeixu/aanet","url":"https://github.com/haofeixu/aanet"}],"syntology":{"n":14,"n_ran":9,"n_unverified":5,"n_pointer_only":0}}},{"leaderboard":"/sota/stereo-disparity-estimation-on-kitti-2015","slug":"stereo-disparity-estimation-on-kitti-2015","dataset":"KITTI 2015","dataset_url":"/dataset/kitti","rows_in_archive":2,"metrics":["D1-all"],"first_row_in_archive_order":{"model":"MoCha-Stereo","paper_title":"MoCha-Stereo: Motif Channel Attention Network for Stereo Matching","paper_url":"/paper/mocha-stereo-motif-channel-attention-network","paper_date":"2024-04-10","arxiv_id":"2404.06842","code_links":[{"title":"zyangchen/mocha-stereo","url":"https://github.com/zyangchen/mocha-stereo"}],"syntology":{"n":17,"n_ran":16,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/stereo-disparity-estimation-on-middlebury","slug":"stereo-disparity-estimation-on-middlebury","dataset":"Middlebury 2014","dataset_url":"/dataset/middlebury-2014","rows_in_archive":2,"metrics":["D1 Error (2px)"],"first_row_in_archive_order":{"model":"MoCha-V2","paper_title":"MoCha-Stereo: Motif Channel Attention Network for Stereo Matching","paper_url":"/paper/mocha-stereo-motif-channel-attention-network","paper_date":"2024-04-10","arxiv_id":"2404.06842","code_links":[{"title":"zyangchen/mocha-stereo","url":"https://github.com/zyangchen/mocha-stereo"}],"syntology":{"n":17,"n_ran":16,"n_unverified":1,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/kitti","name":"KITTI","full_name":"","num_papers_in_archive":3661},{"url":"/dataset/middlebury-2014","name":"Middlebury 2014","full_name":"Middlebury 2014","num_papers_in_archive":59},{"url":"/dataset/spring","name":"Spring","full_name":"Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo","num_papers_in_archive":29},{"url":"/dataset/serv-ct","name":"SERV-CT","full_name":"SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction","num_papers_in_archive":5},{"url":"/dataset/vbr","name":"VBR","full_name":"VBR: A Vision Benchmark in Rome","num_papers_in_archive":3},{"url":"/dataset/guiss-dataset","name":"GUISS dataset","full_name":"Meshes, textures, Blend files, stereo datasets, depth maps, depth estimations)","num_papers_in_archive":1},{"url":"/dataset/l1bsr","name":"L1BSR","full_name":"L1BSR dataset","num_papers_in_archive":1}],"subtasks":[{"url":"/task/stereo-matching-1","name":"Stereo Matching"}],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":19,"of":19,"tagged_in_all":29,"items":[{"url":"/paper/hitnet-hierarchical-iterative-tile-refinement","title":"HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching","date":"2020-07-23","arxiv_id":"2007.12140","repositories_listed":9,"syntology":{"n":5,"n_ran":1,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/spring-a-high-resolution-high-detail-dataset","title":"Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo","date":"2023-03-03","arxiv_id":"2303.01943","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_unverified":2,"n_pointer_only":4}},{"url":"/paper/mocha-stereo-motif-channel-attention-network","title":"MoCha-Stereo: Motif Channel Attention Network for Stereo Matching","date":"2024-04-10","arxiv_id":"2404.06842","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/neural-markov-random-field-for-stereo","title":"Neural Markov Random Field for Stereo Matching","date":"2024-03-17","arxiv_id":"2403.11193","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/an-evaluation-of-deep-learning-based-stereo","title":"An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset","date":"2024-02-19","arxiv_id":"2402.12522","repositories_listed":1,"syntology":null},{"url":"/paper/hinge-wasserstein-mitigating-overconfidence","title":"Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks","date":"2023-06-01","arxiv_id":"2306.00560","repositories_listed":1,"syntology":null},{"url":"/paper/visithers-visible-thermal-infrared-stereo","title":"VisiTherS: Visible-thermal infrared stereo disparity estimation of human silhouette","date":"2023-04-22","arxiv_id":"2304.11291","repositories_listed":1,"syntology":null},{"url":"/paper/stereo-hybrid-event-frame-shef-cameras-for-3d","title":"Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception","date":"2021-10-11","arxiv_id":"2110.04988","repositories_listed":1,"syntology":null},{"url":"/paper/raft-stereo-multilevel-recurrent-field","title":"RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching","date":"2021-09-15","arxiv_id":"2109.07547","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/hierarchical-neural-architecture-search-for-1","title":"Hierarchical Neural Architecture Search for Deep Stereo Matching","date":"2020-10-26","arxiv_id":"2010.13501","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/movement-induced-priors-for-deep-stereo","title":"Movement-induced Priors for Deep Stereo","date":"2020-10-18","arxiv_id":"2010.09105","repositories_listed":1,"syntology":null},{"url":"/paper/stereopagnosia-fooling-stereo-networks-with","title":"Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations","date":"2020-09-21","arxiv_id":"2009.10142","repositories_listed":1,"syntology":null},{"url":"/paper/learning-stereo-matchability-in-disparity","title":"Learning Stereo Matchability in Disparity Regression Networks","date":"2020-08-11","arxiv_id":"2008.04800","repositories_listed":1,"syntology":null},{"url":"/paper/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":2,"n_unverified":7,"n_pointer_only":0}},{"url":"/paper/aanet-adaptive-aggregation-network-for","title":"AANet: Adaptive Aggregation Network for Efficient Stereo Matching","date":"2020-04-20","arxiv_id":"2004.09548","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/depth-based-selective-blurring-in-stereo","title":"Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework","date":"2020-01-21","arxiv_id":"2001.07809","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-algorithm-for-disparity-calculation","title":"A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo vision","date":"2020-01-20","arxiv_id":"2001.06967","repositories_listed":1,"syntology":null},{"url":"/paper/activestereonet-end-to-end-self-supervised","title":"ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems","date":"2018-07-16","arxiv_id":"1807.06009","repositories_listed":1,"syntology":null},{"url":"/paper/sos-stereo-matching-in-o-1-with-slanted","title":"SOS: Stereo Matching in O(1) with Slanted Support Windows","date":"2018-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":8,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","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)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}