论文标题
单眼深度估计挑战
The Monocular Depth Estimation Challenge
论文作者
论文摘要
本文总结了在WACV2023上组织的第一个单眼深度估计挑战（MDEC）的结果。这项挑战评估了挑战性的SYNS-PATCHES数据集对自我监督的单眼深度估计的进展。挑战是在Codalab上组织的，并收到了4个有效团队的意见书。为参与者提供了一个Devkit，其中包含16种最先进算法和4种新技术的更新参考实现。接受新技术的阈值是要优于16个SOTA基准中的每一个。所有参与者在MAE或Absrel等传统指标中的表现都优于基线。但是，PointCloud重建指标具有挑战性。我们发现预测的特征是在对象边界和相对对象定位中的误差下进行插值伪像。我们希望这一挑战对社区做出了宝贵的贡献，并鼓励作者参加以后的版本。
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.
