  The recent advancements in point cloud learning have enabled intelligent
vehicles and robots to comprehend 3D environments better. However, processing
large-scale 3D scenes remains a challenging problem, such that efficient
downsampling methods play a crucial role in point cloud learning. Existing
downsampling methods either require a huge computational burden or sacrifice
fine-grained geometric information. For such purpose, this paper presents an
advanced sampler that achieves both high accuracy and efficiency. The proposed
method utilizes voxel centroid 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 that the sampling results exhibit
a favorable distribution for comprehending various 3D objects or scenes.
Meanwhile, we introduce a network compatible with arbitrary voxel sizes for
sampling and feature extraction while maintaining high efficiency. The proposed
approach is demonstrated with 3D object detection and 3D semantic segmentation.
Compared to existing state-of-the-art methods, our approach achieves better
accuracy on outdoor and indoor large-scale datasets, e.g. Waymo and ScanNet,
with promising efficiency.
