Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces. So in this paper, we propose a multi-frame depth estimation framework which monocular depth can be refined continuously by multi-frame sequential constraints, leveraging a Bayesian fusion layer within several iterations. Both monocular and multi-view networks can be trained with no depth supervision. Our method also enhances the interpretability when combining monocular estimation with multi-view cost volume. Detailed experiments show that our method surpasses state-of-the-art unsupervised methods utilizing single or multiple frames at test time on KITTI benchmark.
@article{arxiv.2305.06036,
title = {FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume},
author = {Zhuofei Huang and Jianlin Liu and Shang Xu and Ying Chen and Yong Liu},
journal= {arXiv preprint arXiv:2305.06036},
year = {2023}
}