Selective refinement queries for volume visualization of unstructured tetrahedral meshes

TitleSelective refinement queries for volume visualization of unstructured tetrahedral meshes
Publication TypeJournal Articles
Year of Publication2004
AuthorsCignoni P, De Floriani L, Magillo P, Puppo E, Scopigno R
JournalVisualization and Computer Graphics, IEEE Transactions on
Pagination29 - 45
Date Published2004///
ISBN Number1077-2626
KeywordsAutomated;Signal Processing, Computer-Assisted;Imaging, Computer-Assisted;User-Computer Interface;, data structure;geometric modeling;interactive visualization;large data sets;multiresolution model;selective refinement queries;unstructured tetrahedral meshes;variable resolution queries;volume data visualization;data visualisation;mesh generation;query p, Three-Dimensional;Online Systems;Pattern Recognition

We address the problem of the efficient visualization of large irregular volume data sets by exploiting a multiresolution model based on tetrahedral meshes. Multiresolution models, also called Level-Of-Detail (LOD) models, allow encoding the whole data set at a virtually continuous range of different resolutions. We have identified a set of queries for extracting meshes at variable resolution from a multiresolution model, based on field values, domain location, or opacity of the transfer function. Such queries allow trading off between resolution and speed in visualization. We define a new compact data structure for encoding a multiresolution tetrahedral mesh built through edge collapses to support selective refinement efficiently and show that such a structure has a storage cost from 3 to 5.5 times lower than standard data structures used for tetrahedral meshes. The data structures and variable resolution queries have been implemented together with state-of-the art visualization techniques in a system for the interactive visualization of three-dimensional scalar fields defined on tetrahedral meshes. Experimental results show that selective refinement queries can support interactive visualization of large data sets.