The Third Monocular Depth Estimation Challenge
This addresses the problem of monocular depth estimation for computer vision researchers, but it is incremental as it builds on prior challenges and methods.
The paper presents results from the third Monocular Depth Estimation Challenge, which tackled zero-shot generalization on the SYNS-Patches dataset, with winners improving 3D F-Score from 17.51% to 23.72%.
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%.