  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%.
