The Second Monocular Depth Estimation Challenge-猫眼课题宝

DOI：

10.1109/cvprw59228.2023.00308

发表时间：

2023-04

期刊：

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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通讯作者：

中科院分区：

其他

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作者：

Jaime Spencer;C. Qian;Michaela Trescakova;Chris Russell;Simon Hadfield;E. Graf;W. Adams;A. Schofield;J. Elder;R. Bowden;Ali Anwar;Hao Chen;Xiaozhi Chen;Kai Cheng;Yuchao Dai;Huynh Thai Hoa;Sadat Hossain;Jian-qiang Huang;Mohan Jing;Bo Li;Chao Li;Baojun Li;Zhiwen Liu;S. Mattoccia;Siegfried Mercelis;Myungwoo Nam;Matteo Poggi;Xiaohua Qi;Jiahui Ren;Yang Tang;Fabio Tosi;L. Trinh;S M Nadim Uddin;Khan Muhammad Umair;Kaixuan Wang;Yufei Wang;Yixing Wang;Mochu Xiang;Guangkai Xu;Wei Yin;Jun Yu;Qi Zhang;Chaoqiang Zhao

本文讨论了第二版单目深度估计挑战赛 (MDEC) 的结果。该版本对使用任何形式的监督的方法开放，包括完全监督、自监督、多任务或代理深度。该挑战基于 SYNS-Patches 数据集，该数据集具有广泛的环境多样性和高质量的密集地面实况。这包括复杂的自然环境，例如森林或田野，这些在当前基准测试中的代表性严重不足。该挑战赛收到了八份独特的提交材料，这些提交材料在任何基于点云或图像的指标上均优于所提供的 SotA 基线。顶级监督提交的相对 F-Score 提高了 27.62%，而顶级自我监督的相对 F-Score 提高了 16.61%。监督提交通常利用大量数据集来提高数据多样性。相反，自我监督的提交更新了网络架构和预训练的主干网。这些结果代表了该领域的重大进展，同时突出了未来研究的途径，例如减少深度边界处的插值伪影、提高自监督室内性能和整体自然图像准确性。

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 pre-trained 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.

The Second Monocular Depth Estimation Challenge

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