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％。

第三届单目深度估计挑战

The Third Monocular Depth Estimation Challenge

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.

本论文介绍了第二届单目深度估计挑战赛（MDEC）的结果。该挑战赛对使用任何形式的监督方法开放，包括全监督、自监督、多任务或代理深度。挑战赛是基于 SYNS-Patches 数据集进行的，该数据集包括高质量密集地面真实度的广泛环境。结果表明，本次挑战赛的提交结果均超过了提供的 SotA 基线，并取得了显着进展，同时强调了未来研究的方向。