PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene. (arXiv:2209.13797v1 [cs.CV])

Fast and efficient semantic segmentation of large-scale LiDAR point clouds is
a fundamental problem in autonomous driving. To achieve this goal, the existing
point-based methods mainly choose to adopt Random Sampling strategy to process
large-scale point clouds. However, our quantative and qualitative studies have
found that Random Sampling may be less suitable for the autonomous driving
scenario, since the LiDAR points follow an uneven or even long-tailed
distribution across the space, which prevents the model from capturing
sufficient information from …

arxiv autonomous autonomous driving driving lidar pcb random sampling segmentation semantic