2019/10/31 by Ramy Battrawy, René Schuster, Battrawy, Ramy +7 · 1 citation
Computer Science · Engineering · #Advanced Image Processing Techniques #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing Techniques and Applications #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1910.14453
openalex publication_date 2019/10/31 · openalex created_date 2022/07/19 · openalex updated_date 2026/07/28
We propose a new approach called LiDAR-Flow to robustly estimate a dense\nscene flow by fusing a sparse LiDAR with stereo images. We take the advantage\nof the high accuracy of LiDAR to resolve the lack of information in some\nregions of stereo images due to textureless objects, shadows, ill-conditioned\nlight environment and many more. Additionally, this fusion can overcome the\ndifficulty of matching unstructured 3D points between LiDAR-only scans. Our\nLiDAR-Flow approach consists of three main steps; each of them exploits LiDAR\nmeasurements. First, we build strong seeds from LiDAR to enhance the robustness\nof matches between stereo images. The imagery part seeks the motion matches and\nincreases the density of scene flow estimation. Then, a consistency check\nemploys LiDAR seeds to remove the possible mismatches. Finally, LiDAR\nmeasurements constraint the edge-preserving interpolation method to fill the\nremaining gaps. In our evaluation we investigate the individual processing\nsteps of our LiDAR-Flow approach and demonstrate the superior performance\ncompared to image-only approach.\n