{"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/tristereonet-a-trinocular-framework-for-multi","title":"TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation","arxiv_id":"2111.12502","date":"2021-11-24","proceeding":null,"authors":["Faranak Shamsafar","Andreas Zell"],"abstract":"Stereo vision is an effective technique for depth estimation with broad applicability in autonomous urban and highway driving. While various deep learning-based approaches have been developed for stereo, the input data from a binocular setup with a fixed baseline are limited. Addressing such a problem, we present an end-to-end network for processing the data from a trinocular setup, which is a combination of a narrow and a wide stereo pair. In this design, two pairs of binocular data with a common reference image are treated with shared weights of the network and a mid-level fusion. We also propose a Guided Addition method for merging the 4D data of the two baselines. Additionally, an iterative sequential self-supervised and supervised learning on real and synthetic datasets is presented, making the training of the trinocular system practical with no need to ground-truth data of the real dataset. Experimental results demonstrate that the trinocular disparity network surpasses the scenario where individual pairs are fed into a similar architecture. Code and dataset: https://github.com/cogsys-tuebingen/tristereonet.","url_abs":"https://arxiv.org/abs/2111.12502v2","url_pdf":"https://arxiv.org/pdf/2111.12502v2.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":"tristereonet-a-trinocular-framework-for-multi","repo_url":"https://github.com/cogsys-tuebingen/tristereonet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"disparity-estimation","task_name":"Disparity Estimation"},{"task_slug":"stereo-depth-estimation","task_name":"Stereo Depth Estimation"},{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/stereo-depth-estimation-on-kitti-2015","task":"Stereo Depth Estimation","dataset":"KITTI 2015","model":"TriStereoNet","rank_in_archive_order":2,"of":2,"metrics":{"D1-all All":"2.35","D1-all Noc":"2.09"},"uses_additional_data":false},{"leaderboard":"/sota/stereo-depth-estimation-on-kitti2015","task":"Stereo Depth Estimation","dataset":"KITTI2015","model":"TriStereoNet","rank_in_archive_order":6,"of":7,"metrics":{"D1-all All":"2.35","D1-all Noc":"2.09"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}