Multiresolution modeling and visualization of volume data

A scalar volumetric dataset is regarded as a sampled version of a function mapping points from a three dimensional domain into a four-dimensional space. We present a multiresolution model for the representation and visualization of volume data that is based on a decomposition of the three-dimensional domain into tetrahedra. Multiresolution is achieved by incorporating a large number of tetrahedralizations that represent the original function at different precisions in the framework of a unified compact model. The model is built through an adaptive incremental approach driven by local coherence. The model supports efficient extraction of isosurfaces, and visualization through projective techniques with adaptive resolution levels, as well as the development of progressive and multiresolution rendering approaches. Experimental results on different datasets are reported.