Robust estimator for 3D meshes filtering

Zhi Hong Mao; Li Zhuang Ma; Ming Xi Zhao

doi:10.1360/jos180453

Robust estimator for 3D meshes filtering

Zhi Hong Mao^*
, Li Zhuang Ma
, Ming Xi Zhao

^*Corresponding author for this work

Shanghai Jiao Tong University

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

Abstract

In this paper, the link between least-squares (LS) estimator and Laplacian smoothing method from the point of view of LS method is firstly disclosed. Further more, an M-estimator for mesh filtering is presented. At last, the M-estimator is extended to the re-weighted M-estimator to remove noise while preserving the surface feature efficiently. The re-weighted M-estimator is bilateral filtering in natural.

Original language	English
Pages (from-to)	453-460
Number of pages	8
Journal	Ruan Jian Xue Bao/Journal of Software
Volume	18
Issue number	2
DOIs	https://doi.org/10.1360/jos180453
State	Published - Feb 2007
Externally published	Yes

Keywords

Feature preserving
Least-squares estimation
M-estimator
Mesh filtering
Re-weighted M-estimator

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10.1360/jos180453

Cite this

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title = "Robust estimator for 3D meshes filtering",

abstract = "In this paper, the link between least-squares (LS) estimator and Laplacian smoothing method from the point of view of LS method is firstly disclosed. Further more, an M-estimator for mesh filtering is presented. At last, the M-estimator is extended to the re-weighted M-estimator to remove noise while preserving the surface feature efficiently. The re-weighted M-estimator is bilateral filtering in natural.",

keywords = "Feature preserving, Least-squares estimation, M-estimator, Mesh filtering, Re-weighted M-estimator",

author = "Mao, \{Zhi Hong\} and Ma, \{Li Zhuang\} and Zhao, \{Ming Xi\}",

year = "2007",

month = feb,

doi = "10.1360/jos180453",

language = "英语",

volume = "18",

pages = "453--460",

journal = "Ruan Jian Xue Bao/Journal of Software",

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publisher = "Chinese Academy of Sciences",

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AU - Mao, Zhi Hong

AU - Ma, Li Zhuang

AU - Zhao, Ming Xi

PY - 2007/2

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N2 - In this paper, the link between least-squares (LS) estimator and Laplacian smoothing method from the point of view of LS method is firstly disclosed. Further more, an M-estimator for mesh filtering is presented. At last, the M-estimator is extended to the re-weighted M-estimator to remove noise while preserving the surface feature efficiently. The re-weighted M-estimator is bilateral filtering in natural.

AB - In this paper, the link between least-squares (LS) estimator and Laplacian smoothing method from the point of view of LS method is firstly disclosed. Further more, an M-estimator for mesh filtering is presented. At last, the M-estimator is extended to the re-weighted M-estimator to remove noise while preserving the surface feature efficiently. The re-weighted M-estimator is bilateral filtering in natural.

KW - Feature preserving

KW - Least-squares estimation

KW - M-estimator

KW - Mesh filtering

KW - Re-weighted M-estimator

UR - https://www.scopus.com/pages/publications/34147202514

U2 - 10.1360/jos180453

DO - 10.1360/jos180453

M3 - 文章

AN - SCOPUS:34147202514

SN - 1000-9825

VL - 18

SP - 453

EP - 460

JO - Ruan Jian Xue Bao/Journal of Software

JF - Ruan Jian Xue Bao/Journal of Software

IS - 2

ER -

Robust estimator for 3D meshes filtering

Abstract

Keywords

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