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.
@article{arxiv.2211.12174,
title = {The Monocular Depth Estimation Challenge},
author = {Jaime Spencer and C. Stella Qian and Chris Russell and Simon Hadfield and Erich Graf and Wendy Adams and Andrew J. Schofield and James Elder and Richard Bowden and Heng Cong and Stefano Mattoccia and Matteo Poggi and Zeeshan Khan Suri and Yang Tang and Fabio Tosi and Hao Wang and Youmin Zhang and Yusheng Zhang and Chaoqiang Zhao},
journal= {arXiv preprint arXiv:2211.12174},
year = {2022}
}