Title: Estimating surface normals in noisy point cloud data
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 t...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.Read More
Publication Year: 2003
Publication Date: 2003-06-08
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 352
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