International Journal of Computational Geometry & Applications; Oct2004, Vol. 14 Issue 4, p261-276, 16p
Subjects
LEAST squares, ESTIMATION theory, SET theory, MANIFOLDS (Mathematics), CURVATURE, and GEOMETRIC surfaces
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 ℝ2 or a smooth surface in ℝ3, 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. [ABSTRACT FROM AUTHOR]