  State-of-the-art methods for driving-scene LiDAR-based perception (including
point cloud semantic segmentation, panoptic segmentation and 3D detection,
\etc) often project the point clouds to 2D space and then process them via 2D
convolution. Although this cooperation shows the competitiveness in the point
cloud, it inevitably alters and abandons the 3D topology and geometric
relations. A natural remedy is to utilize the 3D 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 pattern while maintaining
these inherent properties. The proposed model acts as a backbone and the
learned features from this model can be used for downstream tasks such as point
cloud semantic and panoptic segmentation or 3D detection. In this paper, we
benchmark our model on these three tasks. For semantic segmentation, we
evaluate the proposed model on several large-scale datasets, \ie,
SemanticKITTI, nuScenes and A2D2. Our method achieves the state-of-the-art on
the leaderboard of SemanticKITTI (both single-scan and multi-scan challenge),
and significantly outperforms existing methods on nuScenes and A2D2 dataset.
Furthermore, the proposed 3D framework also shows strong performance and good
generalization on LiDAR panoptic segmentation and LiDAR 3D detection.
