{"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/the-monocular-depth-estimation-challenge","title":"The Monocular Depth Estimation Challenge","arxiv_id":"2211.12174","date":"2022-11-22","proceeding":null,"authors":["Jaime Spencer","C. Stella Qian","Chris Russell","Simon Hadfield","Erich Graf","Wendy Adams","Andrew J. Schofield","James Elder","Richard Bowden","Heng Cong","Stefano Mattoccia","Matteo Poggi","Zeeshan Khan Suri","Yang Tang","Fabio Tosi","Hao Wang","Youmin Zhang","Yusheng Zhang","Chaoqiang Zhao"],"abstract":"This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset. The challenge was organized on CodaLab and received submissions from 4 valid teams. Participants were provided a devkit containing updated reference implementations for 16 State-of-the-Art algorithms and 4 novel techniques. The threshold for acceptance for novel techniques was to outperform every one of the 16 SotA baselines. All participants outperformed the baseline in traditional metrics such as MAE or AbsRel. However, pointcloud reconstruction metrics were challenging to improve upon. We found predictions were characterized by interpolation artefacts at object boundaries and errors in relative object positioning. We hope this challenge is a valuable contribution to the community and encourage authors to participate in future editions.","url_abs":"https://arxiv.org/abs/2211.12174v1","url_pdf":"https://arxiv.org/pdf/2211.12174v1.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":"the-monocular-depth-estimation-challenge","repo_url":"https://github.com/jspenmar/monodepth_benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"mae","method_name":"MAE"}],"datasets_introduced":[{"slug":"syns-patches","name":"SYNS-Patches","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.12174","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}