Estimating surface normals in noisy point cloud data

Niloy J. Mitra; An Nguyen

doi:10.1145/777792.777840

Estimating surface normals in noisy point cloud data

Niloy J. Mitra^*, An Nguyen

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

320 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	322-328
Number of pages	7
DOIs	https://doi.org/10.1145/777792.777840
State	Published - 2003
Externally published	Yes
Event	Nineteenth Annual Symposium on Computational Geometry - san Diego, CA, United States Duration: Jun 8 2003 → Jun 10 2003

Other

Other	Nineteenth Annual Symposium on Computational Geometry
Country/Territory	United States
City	san Diego, CA
Period	06/8/03 → 06/10/03

Keywords

Eigen analysis
Neighborhood size estimation
Noisy data
Normal estimation

ASJC Scopus subject areas

Theoretical Computer Science
Geometry and Topology
Computational Mathematics

Access to Document

10.1145/777792.777840

Cite this

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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.",

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

year = "2003",

doi = "10.1145/777792.777840",

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note = "Nineteenth Annual Symposium on Computational Geometry ; Conference date: 08-06-2003 Through 10-06-2003",

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AU - Mitra, Niloy J.

AU - Nguyen, An

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Y1 - 2003

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 data

KW - Normal estimation

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DO - 10.1145/777792.777840

M3 - Paper

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T2 - Nineteenth Annual Symposium on Computational Geometry

Y2 - 8 June 2003 through 10 June 2003

ER -

Estimating surface normals in noisy point cloud data

Abstract

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Keywords

ASJC Scopus subject areas

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