{"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":"/paper/learning-for-disparity-estimation-through","title":"Learning for Disparity Estimation through Feature Constancy","arxiv_id":"1712.01039","date":"2017-12-04","proceeding":"CVPR 2018 6","authors":["Zhengfa Liang","Yiliu Feng","Yulan Guo","Hengzhu Liu","Wei Chen","Linbo Qiao","Li Zhou","Jianfeng Zhang"],"abstract":"Stereo matching algorithms usually consist of four steps, including matching\ncost calculation, matching cost aggregation, disparity calculation, and\ndisparity refinement. Existing CNN-based methods only adopt CNN to solve parts\nof the four steps, or use different networks to deal with different steps,\nmaking them difficult to obtain the overall optimal solution. In this paper, we\npropose a network architecture to incorporate all steps of stereo matching. The\nnetwork consists of three parts. The first part calculates the multi-scale\nshared features. The second part performs matching cost calculation, matching\ncost aggregation and disparity calculation to estimate the initial disparity\nusing shared features. The initial disparity and the shared features are used\nto calculate the feature constancy that measures correctness of the\ncorrespondence between two input images. The initial disparity and the feature\nconstancy are then fed to a sub-network to refine the initial disparity. The\nproposed method has been evaluated on the Scene Flow and KITTI datasets. It\nachieves the state-of-the-art performance on the KITTI 2012 and KITTI 2015\nbenchmarks while maintaining a very fast running time.","url_abs":"http://arxiv.org/abs/1712.01039v2","url_pdf":"http://arxiv.org/pdf/1712.01039v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-for-disparity-estimation-through","repo_url":"https://github.com/JiaRenChang/PSMNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-for-disparity-estimation-through","repo_url":"https://github.com/leonzfa/iResNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"disparity-estimation","task_name":"Disparity Estimation"},{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"},{"task_slug":"stereo-matching","task_name":"Stereo Matching Hand"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.01039","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}