Abstract
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%.
Abstract (translated)
本文讨论了第三版Monocular Depth Estimation Challenge（MDEC）的结果。该挑战关注于将零散样本到具有挑战性的SYNCSH-Patches数据集，场景位于自然和室内环境中。与前几版一样，方法可以使用任何形式的监督，即监督或自监督。挑战在测试集上总共获得了19个提交，超过了基线：其中10个提交了一份报告，描述了他们的方法，并突出了基础模型如Depth Anything在方法核心中发现的扩散使用情况。挑战获胜者大幅提高了3D F- Score性能，从17.51%到23.72%。
URL
https://arxiv.org/abs/2404.16831
