Estimating surface normals in noisy point cloud data

Niloy Mitra*, An Nguyen

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

217 Scopus citations

Abstract

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ 2 or a smooth surface in ℝ 3 and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.

Original languageEnglish (US)
Pages322-328
Number of pages7
StatePublished - Jul 28 2003
EventNineteenth Annual Symposium on Computational Geometry - san Diego, CA, United States
Duration: Jun 8 2003Jun 10 2003

Other

OtherNineteenth Annual Symposium on Computational Geometry
CountryUnited States
Citysan Diego, CA
Period06/8/0306/10/03

Keywords

  • Eigen analysis
  • Neighborhood size estimation
  • Noisy data
  • Normal estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Geometry and Topology
  • Computational Mathematics

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