Abstract
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.