Depth from a monocular video can enable billions of devices and robots with a
single camera to see the world in 3D. In this paper, we present an approach
with a differentiable flow-to-depth layer for video depth estimation. The model
consists of a flow-to-depth layer, a camera pose refinement module, and a depth
fusion network. Given optical flow and camera pose, our flow-to-depth layer
generates depth proposals and the corresponding confidence maps by explicitly
solving an epipolar geometry optimization problem. Our flow-to-depth layer is
differentiable, and thus we can refine camera poses by maximizing the
aggregated confidence in the camera pose refinement module. Our depth fusion
network can utilize depth proposals and their confidence maps inferred from
different adjacent frames to produce the final depth map. Furthermore, the
depth fusion network can additionally take the depth proposals generated by
other methods to improve the results further. The experiments on three public
datasets show that our approach outperforms state-of-the-art depth estimation
methods, and has reasonable cross dataset generalization capability: our model
trained on KITTI still performs well on the unseen Waymo dataset.

该研究提出了一种基于可微的光流深度预测层的方法，包括光流深度预测层、相机姿态优化模块和深度融合网络，该方法在三个公共数据集上的实验结果表明其性能优于现有的深度估计方法，并具有合理的交叉数据集泛化能力。