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%.

本研究讨论了第三届单目深度估计挑战赛（MDEC）的结果，重点关注挑战赛对具有复杂自然和室内场景的 SYNS-Patches 数据集的零样本泛化。挑战赛接收了总共 19 份提交报告，其中有 10 份报告详细描述了他们的方法，突出了在方法核心使用了 Depth Anything 等基础模型的广泛应用。挑战赛获胜者将 3D F-Score 性能从 17.51％显著提高到 23.72％。