  Depth estimation is a cornerstone for autonomous driving, yet acquiring
per-pixel depth ground truth for supervised learning is challenging.
Self-Supervised Surround Depth Estimation (SSSDE) from consecutive images
offers an economical alternative. While previous SSSDE methods have proposed
different mechanisms to fuse information across images, few of them explicitly
consider the cross-view constraints, leading to inferior performance,
particularly in overlapping regions. This paper proposes an efficient and
consistent pose estimation design and two loss functions to enhance cross-view
consistency for SSSDE. For pose estimation, we propose to use only front-view
images to reduce training memory and sustain pose estimation consistency. The
first loss function is the dense depth consistency loss, which penalizes the
difference between predicted depths in overlapping regions. The second one is
the multi-view reconstruction consistency loss, which aims to maintain
consistency between reconstruction from spatial and spatial-temporal contexts.
Additionally, we introduce a novel flipping augmentation to improve the
performance further. Our techniques enable a simple neural model to achieve
state-of-the-art performance on the DDAD and nuScenes datasets. Last but not
least, our proposed techniques can be easily applied to other methods. The code
will be made public.
