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赛（MDEC）的结果。本届挑战赛接受使用任何监督形式的方法，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，该数据集包含多样化的环境，并提供高质量密集的深度真值。数据集中包括了当前基准测试中严重不足的复杂自然环境（例如森林或田野）。挑战赛收到了八份独到的投稿，这些投稿在基于点云或图像的指标上均超越了所提供的最新基线模型。最佳监督投稿的相对F分数提升了27.62%，而最佳自监督投稿提升了16.61%。监督投稿通常利用大规模数据集集合来提升数据多样性；自监督投稿则通过更新网络架构和预训练骨干网络实现改进。这些成果标志着该领域的显著进展，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、提升自监督方法在室内场景的性能以及整体自然图像的精度。