Unsupervised Spectral Mesh Segmentation Driven by Heterogeneous Graphs

A fully automatic mesh segmentation scheme using heterogeneous graphs is presented. We introduce a spectral framework where local geometry affinities are coupled with surface patch affinities. A heterogeneous graph is constructed combining two distinct graphs: a weighted graph based on adjacency of...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2017-02, Vol.39 (2), p.397-410
Main Authors: Theologou, Panagiotis, Pratikakis, Ioannis, Theoharis, Theoharis
Format: Article
Language:English
Subjects:
3D mesh segmentation
Affinity
Eigenvalues and eigenfunctions
Eigenvectors
Graphs
Image segmentation
Laplace equations
Mesh processing
Segmentation
Shape
Spectral analysis
State of the art
Three-dimensional displays
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Summary:A fully automatic mesh segmentation scheme using heterogeneous graphs is presented. We introduce a spectral framework where local geometry affinities are coupled with surface patch affinities. A heterogeneous graph is constructed combining two distinct graphs: a weighted graph based on adjacency of patches of an initial over-segmentation, and the weighted dual mesh graph. The partitioning relies on processing each eigenvector of the heterogeneous graph Laplacian individually, taking into account the nodal set and nodal domain theory. Experiments on standard datasets show that the proposed unsupervised approach outperforms the state-of-the-art unsupervised methodologies and is comparable to the best supervised approaches.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2016.2544311