Video Depth Estimation by Fusing Flow to Depth Proposals
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 a model for video depth estimation, which consists of a flow to depth layer, a camera pose refinement module, and a depth fusion network. Given optical flow and camera poses, our flow to depth layer generates depth proposals and their 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 the target frame, depth proposals, and confidence maps inferred from different neighboring frames to produce the final depth map. Furthermore, the depth fusion network can additionally take the depth proposals generated by other methods to further improve the results. The experiments on three public datasets show that our approach outperforms state of the art depth estimation methods, and has reasonable crossdataset generalization ability: our model trained on KITTI still performs well on the unseen Waymo dataset.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)