3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the LiDAR sensor and stereo camera for improved depth perception. Instead of directly fusing estimated depths across LiDAR and stereo modalities, we take advantages of the stereo matching network with two enhanced techniques: Input Fusion and Conditional Cost Volume Normalization (CCVNorm) on the LiDAR information. The proposed framework is generic and closely integrated with the cost volume component that is commonly utilized in stereo matching neural networks. We experimentally verify the efficacy and robustness of our method on the KITTI Stereo and Depth Completion datasets, obtaining favorable performance against various fusion strategies. Moreover, we demonstrate that, with a hierarchical extension of CCVNorm, the proposed method brings only slight overhead to the stereo matching network in terms of computation time and model size.

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.