The Second Monocular Depth Estimation Challenge
This work addresses the problem of monocular depth estimation for computer vision applications, reporting incremental progress through a challenge format.
The paper presents results from the second Monocular Depth Estimation Challenge, which tackled monocular depth estimation using various supervision methods on the diverse SYNS-Patches dataset, with top submissions achieving improvements of 27.62% in relative F-Score for supervised and 16.61% for self-supervised approaches.
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.