Estimating surface normals in noisy point cloud data

被引：387

作者：

Mitra, NJ ^{[1
]}

Nguyen, A ^{[1
]}

Guibas, L ^{[1
]}

机构：

[1] Stanford Graph Lab, James H Clark Ctr, Stanford, CA 94305 USA

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL GEOMETRY & APPLICATIONS | 2004年 / 14卷 / 4-5期

关键词：

normal estimation; noisy point cloud data; Eigen analysis; neighborhood size estimation;

D O I：

10.1142/S0218195904001470

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：261 / 276

页数：16

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