  Range-View(RV)-based 3D point cloud segmentation is widely adopted due to its
compact data form. However, RV-based methods fall short in providing robust
segmentation for the occluded points and suffer from distortion of projected
RGB images due to the sparse nature of 3D point clouds. To alleviate these
problems, we propose a new LiDAR and Camera Range-view-based 3D point cloud
semantic segmentation method (LaCRange). Specifically, a
distortion-compensating knowledge distillation (DCKD) strategy is designed to
remedy the adverse effect of RV projection of RGB images. Moreover, a
context-based feature fusion module is introduced for robust and preservative
sensor fusion. Finally, in order to address the limited resolution of RV and
its insufficiency of 3D topology, a new point refinement scheme is devised for
proper aggregation of features in 2D and augmentation of point features in 3D.
We evaluated the proposed method on large-scale autonomous driving datasets \ie
SemanticKITTI and nuScenes. In addition to being real-time, the proposed method
achieves state-of-the-art results on nuScenes benchmark
