This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, including fully-supervised, self-supervised, multi-task or proxy depth. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. This includes complex natural environments, e.g. forests or fields, which are greatly underrepresented in current benchmarks. The challenge received eight unique submissions that outperformed the provided SotA baseline on any of the pointcloud- or image-based metrics. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%. Supervised submissions generally leveraged large collections of datasets to improve data diversity. Self-supervised submissions instead updated the network architecture and pretrained backbones. These results represent a significant progress in the field, while highlighting avenues for future research, such as reducing interpolation artifacts at depth boundaries, improving self-supervised indoor performance and overall natural image accuracy.

翻译：本文讨论了第二届单目深度估计挑战（MDEC）的结果。本届挑战对使用任何形式监督的方法开放，包括全监督、自监督、多任务或代理深度。该挑战基于SYNS-Patches数据集，该数据集涵盖广泛多样的环境，并配有高质量密集的基准真值，包括当前基准中代表性严重不足的复杂自然环境（如森林或田野）。挑战共收到八项独特提交方案，这些方案在任何基于点云或基于图像的指标上均超越了所提供的最先进（SotA）基线。最佳监督提交方案将相对F-Score提升了27.62%，而最佳自监督方案则将其提升了16.61%。监督方案通常利用大量数据集集合以提高数据多样性；自监督方案则更新网络架构和预训练骨干网络。这些结果表明该领域取得了显著进展，同时揭示了未来研究方向，例如减少深度边界处的插值伪影、提升自监督室内性能以及整体自然图像精度。