This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

翻译：本文讨论了第三届单目深度估计挑战赛（MDEC）的结果。该挑战聚焦于对具有挑战性的SYNS-Patches数据集进行零样本泛化，该数据集包含自然场景和室内场景中的复杂场景。与上一届相同，参赛方法可采用任何形式的监督方式（即有监督或自监督）。挑战赛共收到19份在测试集上优于基线的提交方案，其中10份提交了描述其方法的技术报告，突出表明以Depth Anything等基础模型为核心的方案被广泛使用。最终获胜者将3D F-Score性能从17.51%大幅提升至23.72%。