Interactive multiscale tensor reconstruction for multiresolution volume visualization.
Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.
University of Zurich, Switzerland. susuter@ifi.uzh.ch
Susanne K
SK
Suter
José A Iglesias
JA
Guitián
Fabio
F
Marton
Marco
M
Agus
Andreas
A
Elsener
Christoph P E
CP
Zollikofer
M
M
Gopi
Enrico
E
Gobbetti
Renato
R
Pajarola
D000465:Algorithms; D000818:Animals; D003196:Computer Graphics; D003198:Computer Simulation; D016208:Databases, Factual; D015186:Hominidae / Q000033:anatomy & histology; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D008116:Lizards / Q000033:anatomy & histology; D008953:Models, Anatomic; D008963:Molar / Q000033:anatomy & histology; D055114:X-Ray Microtomography / Q000706:statistics & numerical data