Decision trees for accelerating unimodal, hybrid and multimodal rendering models |
| |
Authors: | Maria Ferre Anna Puig Dani Tost |
| |
Affiliation: | (1) Dept. Ingenieria Informàtica i Matemàtiques, URV, Spain;(2) Dept. Matemàtiques Aplicades i Informàtica, UB, Spain;(3) CREB Center of Biomedical Engineering Research, ETSEIB, UPC, Spain |
| |
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. |
| |
Keywords: | Volume rendering Multimodal rendering Hybrid rendering Decision trees Run-length encoding |
本文献已被 SpringerLink 等数据库收录! |
|