Spectral 3D mesh segmentation with a novel single segmentation field

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Hao Wang
  • Tong Lu
  • Oscar Kin-Chung Au
  • Chiew-Lan Tai

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)440-456
Journal / PublicationGraphical Models
Volume76
Issue number5
Online published18 Apr 2014
Publication statusPublished - Sep 2014

Abstract

We present an automatic mesh segmentation framework that achieves 3D segmentation in two stages, hierarchical spectral analysis and isoline-based boundary detection. During the hierarchical spectral analysis stage, a novel segmentation field is defined to capture a concavity-aware decomposition of eigenvectors from a concavity-aware Laplacian. Specifically, a sufficient number of eigenvectors is first adaptively selected and simultaneously partitioned into sub-eigenvectors through spectral clustering. Next, on the sub-eigenvectors level, we evaluate the confidence of identifying a spectral-sensitive mesh boundary for each sub-eigenvector by two joint measures, namely, inner variations and part oscillations. The selection and combination of sub-eigenvectors are thereby formulated as an optimization problem to generate a single segmentation field. In the isoline-based boundary detection stage, the segmentation boundaries are recognized by a divide-merge algorithm and a cut score, which respectively filters and measures desirable isolines from the concise single segmentation field. Experimental results on the Princeton Segmentation Benchmark and a number of other complex meshes demonstrate the effectiveness of the proposed method, which is comparable to recent state-of-the-art algorithms. © 2014 Elsevier Inc. All rights reserved.

Research Area(s)

  • Isoline, Single segmentation field, Spectral analysis, Sub-eigenvector

Citation Format(s)

Spectral 3D mesh segmentation with a novel single segmentation field. / Wang, Hao; Lu, Tong; Au, Oscar Kin-Chung; Tai, Chiew-Lan.

In: Graphical Models, Vol. 76, No. 5, 09.2014, p. 440-456.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review