Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Spectral compression of mesh geometry
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Texture Mapping Using Surface Flattening via Multidimensional Scaling
IEEE Transactions on Visualization and Computer Graphics
A Fast Multi-scale Method for Drawing Large Graphs
GD '00 Proceedings of the 8th International Symposium on Graph Drawing
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Eigenspace Projection Clustering Method for Inexact Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-parameterization and compatible remeshing of 3D models
ACM SIGGRAPH 2004 Papers
A Formulation of Boundary Mesh Segmentation
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Segmentation of 3D Meshes through Spectral Clustering
PG '04 Proceedings of the Computer Graphics and Applications, 12th Pacific Conference
Spectral surface reconstruction from noisy point clouds
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Robust 3D Shape Correspondence in the Spectral Domain
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Unsupervised learning from a corpus for shape-based 3D model retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Fast mesh segmentation using random walks
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Rapid and effective segmentation of 3D models using random walks
Computer Aided Geometric Design
Spectral sampling of manifolds
ACM SIGGRAPH Asia 2010 papers
Spectral sequencing based on graph distance
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
Sampling methods for the Nyström method
The Journal of Machine Learning Research
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In this paper, we apply Nyström method, a sub-sampling and reconstruction technique, to speed up spectral mesh processing. We first relate this method to Kernel Principal Component Analysis (KPCA). This enables us to derive a novel measure in the form of a matrix trace, based soly on sampled data, to quantify the quality of Nyström approximation. The measure is efficient to compute, well-grounded in the context of KPCA, and leads directly to a greedy sampling scheme via trace maximization. On the other hand, analyses show that it also motivates the use of the max-min farthest point sampling, which is a more efficient alternative. We demonstrate the effectiveness of Nyström method with farthest point sampling, compared with random sampling, using two applications: mesh segmentation and mesh correspondence.