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
this https URL

本研究旨在提出一种高效的下采样方法，该方法利用基于体素的采样技术，并通过自适应调整体素大小和保留关键的几何线索来提高采样的准确性和效率。通过在 ShapeNetPart 和 ScanNet 基准测试上进行实验，我们的方法在准确性和效率方面均表现出了优异的性能。