  State-of-the-art methods for large-scale driving-scene LiDAR segmentation
often project the point clouds to 2D space and then process them via 2D
convolution. Although this corporation shows the competitiveness in the point
cloud, it inevitably alters and abandons the 3D topology and geometric
relations. A natural remedy is to utilize the3D voxelization and 3D convolution
network. However, we found that in the outdoor point cloud, the improvement
obtained in this way is quite limited. An important reason is the property of
the outdoor point cloud, namely sparsity and varying density. Motivated by this
investigation, we propose a new framework for the outdoor LiDAR segmentation,
where cylindrical partition and asymmetrical 3D convolution networks are
designed to explore the 3D geometric pat-tern while maintaining these inherent
properties. Moreover, a point-wise refinement module is introduced to alleviate
the interference of lossy voxel-based label encoding. We evaluate the proposed
model on two large-scale datasets, i.e., SemanticKITTI and nuScenes. Our method
achieves the 1st place in the leaderboard of SemanticKITTI and outperforms
existing methods on nuScenes with a noticeable margin, about 4%. Furthermore,
the proposed 3D framework also generalizes well to LiDAR panoptic segmentation
and LiDAR 3D detection.
