Estimating surface normals in noisy point cloud data

Niloy J. Mitra; An Nguyen; Leonidas Guibas

doi:10.1142/s0218195904001470

Estimating surface normals in noisy point cloud data

Niloy J. Mitra^*, An Nguyen, Leonidas Guibas

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

241 Scopus citations

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.

Original language	English (US)
Pages (from-to)	261-276
Number of pages	16
Journal	International Journal of Computational Geometry and Applications
Volume	14
Issue number	4-5
DOIs	https://doi.org/10.1142/s0218195904001470
State	Published - Oct 2004
Externally published	Yes

Keywords

Eigen analysis
Neighborhood size estimation
Noisy point cloud data
Normal estimation

ASJC Scopus subject areas

Theoretical Computer Science
Geometry and Topology
Computational Theory and Mathematics
Computational Mathematics
Applied Mathematics

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10.1142/s0218195904001470

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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 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.",

keywords = "Eigen analysis, Neighborhood size estimation, Noisy point cloud data, Normal estimation",

author = "Mitra, {Niloy J.} and An Nguyen and Leonidas Guibas",

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TY - JOUR

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AU - Mitra, Niloy J.

AU - Nguyen, An

AU - Guibas, Leonidas

PY - 2004/10

Y1 - 2004/10

N2 - 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.

AB - 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.

KW - Eigen analysis

KW - Neighborhood size estimation

KW - Noisy point cloud data

KW - Normal estimation

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U2 - 10.1142/s0218195904001470

DO - 10.1142/s0218195904001470

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JO - International Journal of Computational Geometry and Applications

JF - International Journal of Computational Geometry and Applications

IS - 4-5

ER -

Estimating surface normals in noisy point cloud data

Abstract

Keywords

ASJC Scopus subject areas

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