Point cloud clustering using a panoramic layered range image

Masafumi Nakagawa, Kounosuke Kataoka, Shouta Ouma

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

Abstract

Our aim is to improve region-based point cloud clustering in modeling after point cloud integration. First, we proposed a point cloud clustering methodology on a panoramic layered range image generated with point-based rendering from a massive point cloud. Next, we conducted two experiments to verify our methodology. The results of these experiments confirmed that our proposed methodology can achieve point cloud clustering to extract arbitrary features from complex environments including flat surfaces, slopes and stone steps.

Original languageEnglish
Title of host publication34th Asian Conference on Remote Sensing 2013, ACRS 2013
PublisherAsian Association on Remote Sensing
Pages36-43
Number of pages8
ISBN (Print)9781629939100
Publication statusPublished - 2013 Jan 1
Event34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
Duration: 2013 Oct 202013 Oct 24

Publication series

Name34th Asian Conference on Remote Sensing 2013, ACRS 2013
Volume1

Conference

Conference34th Asian Conference on Remote Sensing 2013, ACRS 2013
CountryIndonesia
CityBali
Period13/10/2013/10/24

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Keywords

  • 3D edge extraction
  • Point cloud clustering
  • Point-based rendering
  • Surface extraction
  • Terrestrial laser scanning

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Nakagawa, M., Kataoka, K., & Ouma, S. (2013). Point cloud clustering using a panoramic layered range image. In 34th Asian Conference on Remote Sensing 2013, ACRS 2013 (pp. 36-43). (34th Asian Conference on Remote Sensing 2013, ACRS 2013; Vol. 1). Asian Association on Remote Sensing.