An Adjustable Farthest Point Sampling Method for Approximately-sorted
Point Cloud Data
- URL: http://arxiv.org/abs/2208.08795v1
- Date: Thu, 18 Aug 2022 12:23:26 GMT
- Title: An Adjustable Farthest Point Sampling Method for Approximately-sorted
Point Cloud Data
- Authors: Jingtao Li, Jian Zhou, Yan Xiong, Xing Chen and Chaitali Chakrabarti
- Abstract summary: We propose adjustable FPS (AFPS), parameterized by M, to aggressively reduce the complexity of FPS without compromising on the sampling performance.
AFPS method can achieve 22 to 30x speedup over original FPS.
We also propose the nearest-point-distance-updating (N) method to limit the number of distance updates to a constant number.
- Score: 13.037325916265639
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Sampling is an essential part of raw point cloud data processing such as in
the popular PointNet++ scheme. Farthest Point Sampling (FPS), which iteratively
samples the farthest point and performs distance updating, is one of the most
popular sampling schemes. Unfortunately it suffers from low efficiency and can
become the bottleneck of point cloud applications. We propose adjustable FPS
(AFPS), parameterized by M, to aggressively reduce the complexity of FPS
without compromising on the sampling performance. Specifically, it divides the
original point cloud into M small point clouds and samples M points
simultaneously. It exploits the dimensional locality of an approximately sorted
point cloud data to minimize its performance degradation. AFPS method can
achieve 22 to 30x speedup over original FPS. Furthermore, we propose the
nearest-point-distance-updating (NPDU) method to limit the number of distance
updates to a constant number. The combined NPDU on AFPS method can achieve a
34-280x speedup on a point cloud with 2K-32K points with algorithmic
performance that is comparable to the original FPS. For instance, for the
ShapeNet part segmentation task, it achieves 0.8490 instance average mIoU (mean
Intersection of Union), which is only 0.0035 drop compared to the original FPS.
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