This paper presents an open and comprehensive framework to systematically evaluate state-of-the-art contributions to self-supervised monocular depth estimation. This includes pretraining, backbone, architectural design choices and loss functions. Many papers in this field claim novelty in either architecture design or loss formulation. However, simply updating the backbone of historical systems results in relative improvements of 25%, allowing them to outperform the majority of existing systems. A systematic evaluation of papers in this field was not straightforward. The need to compare like-with-like in previous papers means that longstanding errors in the evaluation protocol are ubiquitous in the field. It is likely that many papers were not only optimized for particular datasets, but also for errors in the data and evaluation criteria. To aid future research in this area, we release a modular codebase, allowing for easy evaluation of alternate design decisions against corrected data and evaluation criteria. We re-implement, validate and re-evaluate 16 state-of-the-art contributions and introduce a new dataset (SYNS-Patches) containing dense outdoor depth maps in a variety of both natural and urban scenes. This allows for the computation of informative metrics in complex regions such as depth boundaries.
翻译:本文提供了一个开放和全面的框架,以系统评价对自我监督单人深度估计的最新贡献,其中包括培训前、骨干、建筑设计选择和损失功能。这一领域的许多论文都声称在建筑设计或损失配置方面是新颖的。然而,只要更新历史系统的主干系统,就可以相对改进25%,使其优于现有大多数系统。对这一领域的文件进行系统评估并非直截了当。需要将类似以往文件中的类似文件进行比较,这意味着评价协议中长期存在的错误在实地普遍存在。许多论文可能不仅优化特定数据集,而且优化数据和评价标准中的错误。为了帮助这一领域的未来研究,我们发布了一个模块代码库,以便于根据纠正的数据和评价标准评估替代设计决定。我们重新实施、验证和重新评估了16项最新贡献,并引入了一套新的数据集(SYNS-Patches),其中载有各种自然和城市场景的密集户外深度地图。这便于在复杂的区域进行深度测量。