Robust classification of curvilinear and surface-like structures in 3d point cloud data

Mahsa Kamali, Matei Stroila, Jason Cho, Eric Shaffer, John C. Hart

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


The classification of 3d point cloud data is an important component of applications such as map generation and architectural modeling. However, the complexity of the scenes together with the level of noise in the data acquired through mobile laser range-scanning make this task quite difficult. We propose a novel classification method that relies on a combination of edge, node, and relative density information within an Associative Markov Network framework. The main application of our work is the classification of the structures within a point cloud into curvilinear, surface-like, and noise components. We are able to robustly extract complicated structures such as tree branches. The measures taken to ensure the robustness of our method generalize and can be leveraged in noise reduction applications as well. We compare our work with another state of the art classification technique, namely Directional Associative Markov Network, and show that our method can achieve significantly higher accuracy in the classification of the 3d point clouds.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 7th International Symposium, ISVC 2011, Proceedings
Number of pages10
EditionPART 1
StatePublished - Oct 5 2011
Event7th International Symposium on Visual Computing, ISVC 2011 - Las Vegas, NV, United States
Duration: Sep 26 2011Sep 28 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6938 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Symposium on Visual Computing, ISVC 2011
Country/TerritoryUnited States
CityLas Vegas, NV

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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