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A skinning prediction scheme for dynamic 3D mesh compression

  • Institut Mines-Télécom; Télécom SudParis
  • CNRS SAMOVAR UMR 5157

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

6 Citations (Scopus)

Résumé

This paper presents a new prediction-based compression technique for dynamic 3D meshes with constant connectivity and time-varying geometry. The core of the proposed algorithm is a skinning model used for motion compensation. The mesh is first partitioned within vertex clusters that can be described by a single affine motion model. The proposed segmentation technique automatically determines the number of clusters and relays on a decimation strategy privileging the simplification of vertices exhibiting the same affine motion over the whole animation sequence. The residual prediction errors are finally compressed using a temporal-DCT representation. The performances of our encoder are objectively evaluated on a data set of eight animation sequences with various sizes, geometries and topologies, and exhibiting both rigid and elastic motions. The experimental evaluation shows that the proposed compression scheme outperforms state of the art techniques such as MPEG-4/AFX, Dynapack, RT, GV, MCGV, TDCT, PCA and RT compression schemes.

langue originaleAnglais
titreMathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
Les DOIs
étatPublié - 9 nov. 2006
Modification externeOui
EvénementMathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX - San Diego, CA, États-Unis
Durée: 15 août 2006 → 16 août 2006

Série de publications

NomProceedings of SPIE - The International Society for Optical Engineering
Volume6315
ISSN (imprimé)0277-786X

Une conférence

Une conférenceMathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
Pays/TerritoireÉtats-Unis
La villeSan Diego, CA
période15/08/06 → 16/08/06

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