Estimating surface normals in noisy point cloud data

Authors:
Niloy J. Mitra;An Nguyen
Affiliations:
Stanford University, CA;Stanford University, CA
Venue:
Proceedings of the nineteenth annual symposium on Computational geometry
Year:
2003

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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.