Estimating surface normals in noisy point cloud data

Niloy Mitra*, An Nguyen, Leonidas Guibas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

236 Scopus citations


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)
Pages (from-to)261-276
Number of pages16
JournalInternational Journal of Computational Geometry and Applications
Issue number4-5
StatePublished - Oct 1 2004


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Geometry and Topology
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics


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