An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data

Jingtao Li, Jian Zhou, Yan Xiong, Xing Chen, Chaitali Chakrabarti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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) to aggressively reduce the complexity of FPS without compromising the sampling performance. AFPS, parameterized by M, 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. On a multi-core platform, AFPS method with M = 32 can achieve 30× 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. On the ShapeNet part segmentation task, using the NPDU method on AFPS on a point cloud with 2K-32K points helps achieve a 34-280× speedup with 0.8490 instance average mIoU (mean Intersection of Union), which is only 0.0035 lower than that of the original FPS.

Original languageEnglish (US)
Title of host publication2022 IEEE Workshop on Signal Processing Systems, SiPS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665485241
DOIs
StatePublished - 2022
Event36th IEEE Workshop on Signal Processing Systems, SiPS 2022 - Rennes, France
Duration: Nov 2 2022Nov 4 2022

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
Volume2022-November
ISSN (Print)1520-6130

Conference

Conference36th IEEE Workshop on Signal Processing Systems, SiPS 2022
Country/TerritoryFrance
CityRennes
Period11/2/2211/4/22

Keywords

  • 3D Point Cloud
  • Farthest Point Sampling
  • LiDAR Sensor
  • Multi-core Hardware

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture

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