FusionDepth: Complement Self-Supervised Monocular Depth Estimation with
  Cost Volume
Zhuofei Huang Jianlin Liu Shang Xu Ying Chen Yong Liu
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.