  Point cloud analysis (such as 3D segmentation and detection) is a challenging
task, because of not only the irregular geometries of many millions of
unordered points, but also the great variations caused by depth, viewpoint,
occlusion, etc. Current studies put much focus on the adaption of neural
networks to the complex geometries of point clouds, but are blind to a
fundamental question: how to learn an appropriate point embedding space that is
aware of both discriminative semantics and challenging variations? As a
response, we propose a clustering based supervised learning scheme for point
cloud analysis. Unlike current de-facto, scene-wise training paradigm, our
algorithm conducts within-class clustering on the point embedding space for
automatically discovering subclass patterns which are latent yet representative
across scenes. The mined patterns are, in turn, used to repaint the embedding
space, so as to respect the underlying distribution of the entire training
dataset and improve the robustness to the variations. Our algorithm is
principled and readily pluggable to modern point cloud segmentation networks
during training, without extra overhead during testing. With various 3D network
architectures (i.e., voxel-based, point-based, Transformer-based, automatically
searched), our algorithm shows notable improvements on famous point cloud
segmentation datasets (i.e.,2.0-2.6% on single-scan and 2.0-2.2% multi-scan of
SemanticKITTI, 1.8-1.9% on S3DIS, in terms of mIoU). Our algorithm also
demonstrates utility in 3D detection, showing 2.0-3.4% mAP gains on KITTI.
