Geometry compression of tetrahedral meshes using optimized prediction

Dan Chen, Yi Jen Chiang, Nasir Memon, Xiaolin Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we propose a novel geometry compression technique for volumetric datasets represented as tetrahedral meshes. We focus on a commonly used technique for predicting vertex geometries via a fiipping operation using an extension of the parallelogram rule. We demonstrate that the efficiency of the fiipping operation is dependent on the order in which tetrahedra are traversed and vertices are predicted accordingly. We formulate the problem of optimally (traversing tetrahedra and) predicting the vertices via fiippings as a combinatorial optimization problem of constructing a constrained minimum spanning tree. We give heuristic solutions for this problem and show that we can achieve prediction efficiency very close to that of the unconstrained minimum spanning tree which is an unachievable lower bound. We also show significant improvements of our new geometry compression over the state-of-the-art fiipping approach, whose traversal order does not take into account the geometry of the mesh.

Original languageEnglish (US)
Title of host publication13th European Signal Processing Conference, EUSIPCO 2005
Pages1906-1909
Number of pages4
StatePublished - 2005
Event13th European Signal Processing Conference, EUSIPCO 2005 - Antalya, Turkey
Duration: Sep 4 2005Sep 8 2005

Publication series

Name13th European Signal Processing Conference, EUSIPCO 2005

Other

Other13th European Signal Processing Conference, EUSIPCO 2005
CountryTurkey
CityAntalya
Period9/4/059/8/05

ASJC Scopus subject areas

  • Signal Processing

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