Mesh Segmentation via Spectral Embedding and Contour Analysis

By Rong Liu, Hao Zhang. 2007

146 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Environmental Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Leonidas J. Guibas
2. Hao Su
3. Li Yi
4. Hao Zhang
5. Michael M. Bronstein
6. Charles Ruizhongtai Qi
7. Bruno Lévy
8. Michela Spagnuolo

In Computer Graphics and Computer-Aided Design

1. Leonidas J. Guibas
2. Bruno Lévy
3. Li Yi
4. Hao Su
5. Michela Spagnuolo
6. Silvia Biasotti
7. Daniela Giorgi
8. Hao Zhang

In Environmental Engineering

1. Bruno Lévy
2. Hao Su
3. Li Yi
4. Leonidas J. Guibas
5. Xingwen Guo
6. Michela Spagnuolo
7. Hao Zhang

Citations in the corpus

• SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation (2016, methodology): “In the domain of unsupervised shape segmentation, there is one family of methods [15, 16] emphasizing the effectiveness of spectral analysis for 3D shape segmentation.”
• Technical Section: Discrete Laplace-Beltrami operators for shape analysis and segmentation (2009, methodology): “Spectral methods have recently gained much interest in computer graphics [62], with applications that include mesh compression [30], parametrization [25,43], segmentation[31,35], remeshing [17], filtering [34,57],”
• Spectral Mesh Processing (2009, methodology): “…A in Frobenius norm Mesh projected into the eigenspace formed by the first two eigenvectors of a mesh Laplacian Reduce 3D analysis to contour analysis [Liu & Zhang 07] Main references Overview A signal processing perspective A very gentle introduction . boredom alert Signal…”