AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Point Cloud Analysis

Efficient downsampling plays a crucial role in point cloud learning, particularly for large-scale 3D scenes. Existing downsampling methods either require a huge computational burden or sacrifice fine-grained geometric information. This paper presents an advanced sampler that achieves both high accuracy and efficiency. The proposed method utilizes voxel-based sampling as a foundation, but effectively addresses the challenges regarding voxel size determination and the preservation of critical geometric cues. Specifically, we propose a Voxel Adaptation Module that adaptively adjusts voxel sizes with the reference of point-based downsampling ratio. This ensures the sampling results exhibit a favorable distribution for comprehending various 3D objects or scenes. Additionally, we introduce a network compatible with arbitrary voxel sizes for sampling and feature extraction while maintaining high efficiency. Our method achieves state-of-the-art accuracy on the ShapeNetPart and ScanNet benchmarks with promising efficiency. Code will be available at https://github.com/yhc2021/AVS-Net.