Video Depth Estimation by Fusing Flow-to-Depth Proposals

We present an approach with a novel 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. Unlike other methods, our flow-to-depth layer is differentiable, and thus we can refine camera poses by maximizing the aggregated confidence in 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 strong generalization capability: our model trained on KITTI performs well on the unseen Waymo dataset while other methods degenerate a lot.