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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Communications of the ACM - Special issue on digital multimedia systems
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SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Spectral compression of mesh geometry
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Geometry-Aware Bases for Shape Approximation
IEEE Transactions on Visualization and Computer Graphics
Fast direct policy evaluation using multiscale analysis of Markov diffusion processes
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discovering Process Models from Unlabelled Event Logs
BPM '09 Proceedings of the 7th International Conference on Business Process Management
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This paper investigates compression of 3D objects in computer graphics using manifold learning. Spectral compression uses the eigenvectors of the graph Laplacian of an object's topology to adaptively compress 3D objects. 3D compression is a challenging application domain: object models can have 105 vertices, and reliably computing the basis functions on large graphs is numerically challenging. In this paper, we introduce a novel multiscale manifold learning approach to 3D mesh compression using diffusion wavelets, a general extension of wavelets to graphs with arbitrary topology. Unlike the "global" nature of Laplacian bases, diffusion wavelet bases are compact, and multiscale in nature. We decompose large graphs using a fast graph partitioning method, and combine local multiscale wavelet bases computed on each subgraph. We present results showing that multiscale diffusion wavelets bases are superior to the Laplacian bases for adaptive compression of large 3D objects.