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A novel feature point detection algorithm of unstructured 3D point cloud

  • Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Compared with 3D mesh data, unstructured point cloud data lack adjacency relationship between points, which only contain geometric coordinates and little information. This paper focuses on the research of characteristics of unstructured point cloud detection algorithm. We put forward the multiscale 3D Harris feature point detection algorithm, which uses iteration strategy to select the optimal Harris response value in multiple scales. Compared with the classical 3D Harris feature point detection algorithm for mesh data, our algorithm can fully use the local information of point cloud models to detect feature point on point cloud models. It is very robust to rotation transformation of point clouds and noise.

Original languageEnglish
Title of host publicationIntelligent Computing Methodologies - 12th International Conference, ICIC 2016, Proceedings
EditorsAbir Hussain, Kyungsook Han, De-Shuang Huang
PublisherSpringer Verlag
Pages736-744
Number of pages9
ISBN (Print)9783319422961
DOIs
StatePublished - 2016
Event12th International Conference on Intelligent Computing Methodologies, ICIC 2016 - Lanzhou, China
Duration: 2 Aug 2016 → 5 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9773
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Computing Methodologies, ICIC 2016
Country/TerritoryChina
CityLanzhou
Period2/08/16 → 5/08/16

Keywords

  • 3D point cloud
  • Feature point detection
  • Harris
  • Multiscale
  • Unstructured

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