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.

本篇论文提出了一个基于多帧深度估计的框架，使用连续的多帧约束不断优化单目深度估计，利用贝叶斯融合层进行多帧深度估计的迭代，同时不需要深度监督训练。实验结果表明，该方法在 KITTI 基准测试中，相较于单个帧或多个帧的无监督方法，取得了更好的性能。