Multi-Dimensional Trees for Controlled Volume Rendering and Compression

This paper explores the use of multi-dimensional trees to provide spatial and temporal e ciencies in imaging large data sets. Each node of the tree contains a model of the data in terms of a xed number of basis functions, a measure of the error in that model, and a measure of the importance of the data in the region covered by the node. A divide-and-conquer algorithm permits e cient computation of these quantities at all nodes of the tree. The exible design permits various sets of basis functions, error criteria, and importance criteria to be implemented easily. Selective traversal of the tree provides images in acceptable time, by drawing nodes that cover a large volume as single objects when the approximation error and/or importance are low, and descending to ner detail otherwise. Trees over very large datasets can be pruned by the same criterion to provide data representations of acceptable size and accuracy. Compression and traversal are controlled by a userde ned combination of m...

• Type: Conference
• Year: 1995
• Where: VVS
• Authors: Jane Wilhelms, Allen Van Gelder
• Keywords: Basis Functions, Error And/or Importance, Large Data Sets, Visualization, VVS 1995