Procedural Volume Encoding ,
Yun Jang

June 13 (Mon) / 1:30 p.m.



Abstract :

Recent improvements in computational capability have given scientists increase dability to simulate large-scale, complex, real world phenomena. The data sets generated from these simulations vary in structure and organization, and rendering these complex topological connectivities is still a challenging problem. In this work however, we move away from the traditional methods of visualizing simulation data, and present a novel approach that procedurally encodes the simulation data and discards the underlying grid and connectivity information. This encoding enables interactive manipulation and rendering of these large-scale simulations, and requires significantly less storage than that needed for the original data representation. This paper presents the complete process required to procedurally encode volumetric data using radial basis functions (RBFs). Several solution techniques for obtaining parameters needed in the RBF representations are described. These techniques range from low-cost clustering algorithms to computationally expensive nonlinear optimizations. Moreover, with the compact and accurate RBF functional representations of large-scale complex data, the interactive rendering of the data can be performed with desktop PCs utilizing commodity graphics hardware.


Yun Jang

Graduate Research Assistant, Purdue University

Potter 134, PURPL (Purdue University Rendering & Peceptualization Lab)

E-mail: , Tel: (765) 494-5943



BS in Electrical Engineering, Seoul National University, Korea, February 2000

MS in Electrical and Computer Engineering, Purdue University, May 2002

Research Interests:

Interactive procedural visualization of terabyte datasets

Flow visualization

Volume encoding