  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.
