Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud Classification

Understand

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.