LiDARFlow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images We propose a new approach called LiDARFlow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images. We take the advantage of the high accuracy of LiDAR to resolve the lack of information in some regions of stereo images due to textureless objects, shadows, illconditioned light environment and many more. Additionally, this fusion can overcome the difficulty of matching unstructured 3D points between LiDARonly scans. Our LiDARFlow approach consists of three main steps; each of them exploits LiDAR measurements. First, we build strong seeds from LiDAR to enhance the robustness of matches between stereo images. The imagery part seeks the motion matches and increases the density of scene flow estimation. Then, a consistency check employs LiDAR seeds to remove the possible mismatches. Finally, LiDAR measurements constraint the edgepreserving interpolation method to fill the remaining gaps. In our evaluation we investigate the individual processing steps of our LiDARFlow approach and demonstrate the superior performance compared to imageonly approach.

Learning Residual Flow as Dynamic Motion from Stereo Videos We present a method for decomposing the 3D scene flow observed from a moving stereo rig into stationary scene elements and dynamic object motion. Our unsupervised learning framework jointly reasons about the camera motion, optical flow, and 3D motion of moving objects. Three cooperating networks predict stereo matching, camera motion, and residual flow, which represents the flow component due to object motion and not from camera motion. Based on rigid projective geometry, the estimated stereo depth is used to guide the camera motion estimation, and the depth and camera motion are used to guide the residual flow estimation. We also explicitly estimate the 3D scene flow of dynamic objects based on the residual flow and scene depth. Experiments on the KITTI dataset demonstrate the effectiveness of our approach and show that our method outperforms other stateoftheart algorithms on the optical flow and visual odometry tasks.