N. J. Mitra and A. Nguyen, Estimating Surface Normals in Noisy Point Cloud Data, Symposium on COmputational Geometry, pp. 322-328, 2003.

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 pres- ence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal esti- mation 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.

Bibtex: 

@INPROCEEDINGS{mn-esnnpcd-03,
  AUTHOR =       "N.~J.~Mitra and A.~Nguyen",
  TITLE =        "Estimating Surface Normals in Noisy Point Cloud Data",
  BOOKTITLE =    "Symposium on COmputational Geometry",
  YEAR =         "2003",
  PAGES=  "322--328"
}