  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 points in different distance ranges and reduces the
model's learning capability. To alleviate this problem, we propose a new Polar
Cylinder Balanced Random Sampling method that enables the downsampled point
clouds to maintain a more balanced distribution and improve the segmentation
performance under different spatial distributions. In addition, a sampling
consistency loss is introduced to further improve the segmentation performance
and reduce the model's variance under different sampling methods. Extensive
experiments confirm that our approach produces excellent performance on both
SemanticKITTI and SemanticPOSS benchmarks, achieving a 2.8% and 4.0%
improvement, respectively. The source code is available at
https://github.com/huixiancheng/PCB-RandNet.
