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13

COMPGEOM
2003
ACM

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146views Discrete Geometry» more COMPGEOM 2003»

Estimating surface normals in noisy point cloud data

13 years 9 months ago

Estimating surface normals in noisy point cloud data

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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 2 or a smooth surface in 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. Categories and Subject Descriptors I.3.5 [ Computing Methodologies ]: Computer Graphics Computational Geometry and Object Modeling [Curve, surface, solid, and object representations] Keywords normal estimation, noisy data, eigen analysis, neighborhood size estimation

Niloy J. Mitra, An Nguyen

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COMPGEOM 2003 | Neighborhood Size | Normal Estimation | Optimal Neighborhood Size |

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Added	05 Jul 2010
Updated	05 Jul 2010
Type	Conference
Year	2003
Where	COMPGEOM
Authors	Niloy J. Mitra, An Nguyen

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