Shape Analysis Using the Spectral Graph Wavelet Transform

Leandro, Jorge de Jesus Gomes; César, Roberto M.; Feris, Rogério

doi:10.1109/escience.2013.45

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…We exploit both temporal and spatial redundancy to effectively reduce DMS data size, proposing temporal frame‐clustering and spatial decorrelation algorithms. We also employ spectral graph wavelet transformation (SGWT) with color set partitioning embedded block (CSPECK) encoding to turn resultant DMS into a multiresolution representation, supporting progressive streaming. Our main contributions include the following: Temporal frame clustering : We extract and organize mesh vertex trajectories of a DMS into frame clusters such that temporal redundancy of mesh motions can be identified and removed by principal component analysis (PCA) dimensionality reduction. Spatial decorrelation : We transform coordinates of mesh vertex trajectories into a decorrelated space, identifying independent parts of spatial information about mesh vertex trajectories together with their importance.…”

Section: Introductionmentioning

confidence: 99%

Compressed dynamic mesh sequence for progressive streaming

Yang

Jiang

Shangguan

et al. 2018

Computer Animation & Virtual

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Dynamic mesh sequence (DMS) is a simple and accurate representation for precisely recording a 3D animation sequence. Despite its simplicity, this representation is typically large in data size, making storage and transmission expensive. This paper presents a novel framework that allows effective DMS compression and progressive streaming by eliminating spatial and temporal redundancy. To explore temporal redundancy, we propose a temporal frame-clustering algorithm to organize DMS frames by their motion trajectory changes, eliminating intracluster redundancy by principal component analysis dimensionality reduction. To eliminate spatial redundancy, we propose an algorithm to transform the coordinates of mesh vertex trajectory into a decorrelated trajectory space, generating a new spatially nonredundant trajectory representation. We finally apply a spectral graph wavelet transform with color set partitioning embedded block encoding to turn the resultant DMS into a multiresolution representation to support progressive streaming. Experiment results show that our method outperforms several existing methods in terms of storage requirement and reconstruction quality. KEYWORDSPCA, progressive streaming, spectrum wavelet transform, 3D mesh coding, 3D mesh sequence compression Comput Anim Virtual Worlds. 2019;30:e1847.wileyonlinelibrary.com/journal/cav

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