5 accepted papers
Recent multimodal fusion methods, integrating images with LiDAR point clouds, have shown promise in scene flow estimation. However, the fusion of 4D millimeter wave radar and LiDAR remains unexplored. Unlike LiDAR, radar is cheaper, more robust in various weather conditions and can detect point-wise
Due to the difficulty of obtaining ground-truth data for 4D radar scene flow estimation, previous methods typically rely on either self-supervised losses or cross-modal supervision using 3D LiDAR data, 2D images, and odometry. However, self-supervised approaches often yield suboptimal results due to…
Despite the great success of deep learning in stereo matching recovering accurate disparity maps is still challenging. Currently L1 and cross-entropy are the two most widely used losses for stereo network training. Compared with the former the latter usually performs better thanks to its probability…
As a low-level task of 3D perception, scene flow is a fundamental representation of dynamic scenes and provides non-rigid motion descriptions for the objects in the 3D environment, which can strongly support many upper-level applications. Inspired by the revolutionary success of deep learning, many
Visual relocalization aims to estimate the absolute camera pose from an image or sequential images. Recent works tackle this problem by exploiting deep neural networks to regress camera poses. However, spatial and temporal clues from sequential images still remain underexplored, resulting in inaccur