Occlusion-Aware Unsupervised Learning of Monocular Depth, Optical Flow and Camera Pose with Geometric Constraints

We present an occlusion-aware unsupervised neural network for jointly learning three low-level vision tasks from monocular videos: depth, optical flow, and camera motion. The system consists of three different predicting sub-networks simultaneously coupled by combined loss terms and is capable of computing each task independently on test samples. Geometric constraints extracted from scene geometry which have traditionally been used in bundle adjustment or pose-graph optimization are formed as various self-supervisory signals during our end-to-end learning approach. Different from prior works, our image reconstruction loss also takes account of optical flow. Moreover, we impose novel 3D flow consistency constraints over the predictions of all the three tasks. By explicitly modeling occlusion and taking utilization of both 2D and 3D geometry relationships, abundant geometric constraints are formed over estimated outputs, enabling the system to capture both low-level representations and high-level cues to infer thinner scene structures. Empirical evaluation on the KITTI dataset demonstrates the effectiveness and improvement of our approach: (1) monocular depth estimation outperforms state-of-the-art unsupervised methods and is comparable to stereo supervised ones; (2) optical flow prediction ranks top among prior works and even beats supervised and traditional ones especially in non-occluded regions; (3) pose estimation outperforms established SLAM systems under comparable input settings with a reasonable margin.