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Interactive multiscale tensor reconstruction for multiresolution volume visualization


Suter, S K; Guitian, J A; Marton, F; Agus, M; Elsener, A; Zollikofer, C P E; Gopi, M; Gobbetti, E; Pajarola, R (2011). Interactive multiscale tensor reconstruction for multiresolution volume visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12):2135-2143.

Abstract

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.

Abstract

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.

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14 citations in Web of Science®
12 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
07 Faculty of Science > Department of Anthropology
Dewey Decimal Classification:000 Computer science, knowledge & systems
300 Social sciences, sociology & anthropology
Language:English
Date:2011
Deposited On:26 Jan 2012 14:27
Last Modified:05 Apr 2016 15:25
Publisher:IEEE
ISSN:1077-2626
Publisher DOI:https://doi.org/10.1109/TVCG.2011.214
Other Identification Number:merlin-id:4920

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