Decision trees for accelerating unimodal, hybrid and multimodal rendering models
Maria Ferre, Anna Puig, Dani Tost
In The Visual Computer, 22(3), 2006.
Abstract: This paper deals with the rendering of segmented unimodal, hybrid and aligned multimodal voxel models. We propose a data structure that classifies the segmented voxels into categories, so that whenever the model has to be traversed, only the selected categories are visited and the empty and non-selected voxels are skipped. This strategy is based on: (i) a decision tree, called the rendering decision tree (RDT), which represents the hierarchy of the classification process and (ii) an intermediate run-length encoding (RLE) of the classified voxel model. The traversal of the voxel model given a user query consists of two steps: first, the RDT is traversed and the set of selected categories computed; next, the RLE is visited, but the non-selected runs are skipped and only the voxels of the original model that are codified are accessed in selected runs of the RLE. This strategy has been used to render a voxel model by back-to-front traversal and splatting as well as to construct 3D textures for hardware-driven 3D texture mapping. The results show that the voxel model traversal is significantly accelerated.
Keyword(s): Volume rendering, Multimodal rendering, Hybrid rendering, Decision trees, Run-length encoding
@article{Ferre:2006:DTF,
author = {Maria Ferre and Anna Puig and Dani Tost},
title = {Decision trees for accelerating unimodal, hybrid and multimodal rendering models},
journal = {The Visual Computer},
volume = {22},
number = {3},
pages = {158--167},
year = {2006},
}
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