Article

Adaptive mesh compression in 3D computer graphics using multiscale manifold learning

Author:

Sridhar MahadevanAuthors Info & Claims

ICML '07: Proceedings of the 24th international conference on Machine learning

Pages 585 - 592

https://doi.org/10.1145/1273496.1273570

Published: 20 June 2007 Publication History

Abstract

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.

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Cited By

Choukroun YPai GKimmel R(2018)Sparse Approximation of 3D Meshes Using the Spectral Geometry of the Hamiltonian OperatorJournal of Mathematical Imaging and Vision10.1007/s10851-018-0822-060:6(941-952)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10851-018-0822-0
Choukroun YPai GKimmel RMitra NRitschel TBærentzen JHildebrandt K(2017)Schrödinger operator for sparse approximation of 3D meshesProceedings of the Symposium on Geometry Processing: Posters10.2312/sgp.20171205(9-10)Online publication date: 3-Jul-2017
https://dl.acm.org/doi/10.2312/sgp.20171205
Maglo ALavoué GDupont FHudelot C(2015)3D Mesh CompressionACM Computing Surveys10.1145/269344347:3(1-41)Online publication date: 17-Feb-2015
https://dl.acm.org/doi/10.1145/2693443
Show More Cited By

Adaptive mesh compression in 3D computer graphics using multiscale manifold learning
1. Computing methodologies

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Published In

cover image ACM Other conferences

ICML '07: Proceedings of the 24th international conference on Machine learning

June 2007

1233 pages

ISBN:9781595937933

DOI:10.1145/1273496

Editor:
Zoubin Ghahramani
University of Cambridge, United Kingdom

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2007

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ICML '07 & ILP '07

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ICML '07 & ILP '07: The 24th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 20 - 24, 2007

Oregon, Corvalis, USA

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Cited By

Choukroun YPai GKimmel R(2018)Sparse Approximation of 3D Meshes Using the Spectral Geometry of the Hamiltonian OperatorJournal of Mathematical Imaging and Vision10.1007/s10851-018-0822-060:6(941-952)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10851-018-0822-0
Choukroun YPai GKimmel RMitra NRitschel TBærentzen JHildebrandt K(2017)Schrödinger operator for sparse approximation of 3D meshesProceedings of the Symposium on Geometry Processing: Posters10.2312/sgp.20171205(9-10)Online publication date: 3-Jul-2017
https://dl.acm.org/doi/10.2312/sgp.20171205
Maglo ALavoué GDupont FHudelot C(2015)3D Mesh CompressionACM Computing Surveys10.1145/269344347:3(1-41)Online publication date: 17-Feb-2015
https://dl.acm.org/doi/10.1145/2693443
Gudivada SBors A(2015)Ortho-diffusion decompositions for face recognition from low quality images2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7351480(3625-3629)Online publication date: Oct-2015
https://doi.org/10.1109/ICIP.2015.7351480
Gudivada SBors A(2015)Ortho-diffusion decompositions of graph-based representation of imagesPattern Recognition10.1016/j.patcog.2015.03.02248:12(4097-4115)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1016/j.patcog.2015.03.022
Zhong MQin H(2014)Sparse approximation of 3D shapes via spectral graph waveletsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-014-0971-030:6-8(751-761)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1007/s00371-014-0971-0
Mahadevan S(2008)Representation Discovery using Harmonic AnalysisSynthesis Lectures on Artificial Intelligence and Machine Learning10.2200/S00130ED1V01Y200806AIM0042:1(1-147)Online publication date: Jan-2008
https://doi.org/10.2200/S00130ED1V01Y200806AIM004

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