The Monocular Depth Estimation Challenge

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

Research output: Unpublished contribution to conferenceUnpublished Conference Paperpeer-review


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.
Original languageEnglish
Number of pages10
Publication statusPublished - 7 Feb 2023
Event2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) - Waikoloa, HI, USA
Duration: 3 Jan 20237 Jan 2023


Conference2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)


  • measurement
  • interpolation
  • computer vision
  • estimation
  • Prediction algorithms
  • conferences


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