  Great progress has been made in point cloud classification with
learning-based methods. However, complex scene and sensor inaccuracy in
real-world application make point cloud data suffer from corruptions, such as
occlusion, noise and outliers. In this work, we propose Point-Voxel based
Adaptive (PV-Ada) feature abstraction for robust point cloud classification
under various corruptions. Specifically, the proposed framework iteratively
voxelize the point cloud and extract point-voxel feature with shared local
encoding and Transformer. Then, adaptive max-pooling is proposed to robustly
aggregate the point cloud feature for classification. Experiments on ModelNet-C
dataset demonstrate that PV-Ada outperforms the state-of-the-art methods. In
particular, we rank the $2^{nd}$ place in ModelNet-C classification track of
PointCloud-C Challenge 2022, with Overall Accuracy (OA) being 0.865. Code will
be available at https://github.com/zhulf0804/PV-Ada.
