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.
翻译:本文讨论了单目深度估计挑战赛(MDEC)第二季的结果。这一季度的挑战赛针对任何形式的监督方法,包括完全监督、自监督、多任务或代理深度等。挑战赛基于SYNS-Patches数据集展开,该数据集包含了丰富的环境类型,并且具有高质量的密集地面真实值。这些类型包括自然环境,如森林或田野,这是当前基准测试中极少出现的。本次挑战赛收到了八个独特的提交,均在基于点云或图像的任何度量标准上优于提供的SotA基线。顶级的监督提交提高了27.62%的相对F-Score,而自监督提交则提高了16.61%。监督提交通常利用大量的数据集来提高数据多样性。自监督提交则更新了网络架构和预训练骨干。这些结果代表了领域内的显着进展,同时也突显了未来研究的方向,如减少深度边界处的内插伪影,提高自监督室内性能和整体自然图像的准确性。