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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"
}
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