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.
翻译:本文件总结了在WACV2023举办的第一次单人深度估计挑战(MECC)的结果,评估了对具有挑战性的SYNS-Patches数据集进行自我监督的单眼深度估计的进展,在CodaLab上组织了这项挑战,并收到了4个有效小组的呈件,向与会者提供了载有16种最先进的算法和4种新技术的最新参考实施标准。接受新技术的门槛是超过16个SotA基线中的每一项。所有参与者都完成了传统指标(如MAE或AbsRel)的基线。然而,点球重建指标难以改进。我们发现预测的特点是物体边界上的相互交错和相对物体定位的错误。我们希望这一挑战对社区是一个宝贵的贡献,并鼓励作者参加今后的版本。