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.
Bibtex:
@INPROCEEDINGS{mng_est_normal_ijcga_04,
AUTHOR = "N. J. Mitra and A. Nguyen and L. Guibas",
TITLE = "Estimating Surface Normals in Noisy Point Cloud Data",
BOOKTITLE = "special issue of International Journal of Computational Geometry and Applications",
volume = "14",
number = "4--5",
pages = "261--276",
YEAR = "2004"
}
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