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Adaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold Learning
Published on Jun 23, 20079039 Views
This paper investigates compression of 3D ob jects in computer graphics using manifold learning. Spectral compression uses the eigenvectors of the graph Laplacian of an object's topology to adaptively
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Adaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold Learning 00:00
Overview00:06
Representing 3D Objects00:37
Representing 3D Objects01:48
A “Large” 3D Object01:54
Compression Methods02:06
Applications of Graph Laplacian (Fiedler, 1973)02:42
Differential Coordinate Representation 03:08
Random Walks and the Graph Laplacian03:44
Eigenvectors of Graph Laplacian on 3D Object 04:32
Laplacian Compression of 3D Objects05:06
Limitation of Laplacian Bases05:46
Wavelet Analysis on Graphs06:42
Beyond Eigenvectors: Diffusion Wavelets (Coifman and Maggioni, ACHA 2006; Mahadevan and Maggioni, ICML 2006, NIPS 2006)08:05
Diffusion Wavelets (Coifman and Maggioni, ACHA, 2006 ; Maggioni and Mahadevan, ICML 2006, NIPS 2006) 10:23
Multiscale Diffusion Basis11:22
Feature Discovery: Level 5 Basis Functions11:43
Fourier vs. Wavelet Compression12:02
Compression of Large Objects12:33
Scaling to Large 3D Objects: Fourier vs wavelet bases13:17
Scaling to Large 3D Objects: Fourier vs wavelet bases14:27
Error vs. Number of Partitions14:45
Summary15:35