Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Fast isocontouring for improved interactivity
Proceedings of the 1996 symposium on Volume visualization
Isosurfacing in span space with utmost efficiency (ISSUE)
Proceedings of the 7th conference on Visualization '96
High quality rendering of attributed volume data
Proceedings of the conference on Visualization '98
A fast volume rendering algorithm for time-varying fields using a time-space partitioning (TSP) tree
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
A practical evaluation of popular volume rendering algorithms
VVS '00 Proceedings of the 2000 IEEE symposium on Volume visualization
FastSplats: optimized splatting on rectilinear grids
Proceedings of the conference on Visualization '00
Data Structures and Algorithm Analysis in C++
Data Structures and Algorithm Analysis in C++
Global static indexing for real-time exploration of very large regular grids
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Proceedings of the conference on Visualization '01
Automatic Isosurface Propagation Using an Extrema Graph and Sorted Boundary Cell Lists
IEEE Transactions on Visualization and Computer Graphics
A Near Optimal Isosurface Extraction Algorithm Using the Span Space
IEEE Transactions on Visualization and Computer Graphics
Speeding Up Isosurface Extraction Using Interval Trees
IEEE Transactions on Visualization and Computer Graphics
High-Quality Splatting on Rectilinear Grids with Efficient Culling of Occluded Voxels
IEEE Transactions on Visualization and Computer Graphics
Accelerated Isosurface Extraction in Time-Varying Fields
IEEE Transactions on Visualization and Computer Graphics
Interactive Texture-Based Volume Rendering for Large Data Sets
IEEE Computer Graphics and Applications
A Parallel Coordinates Style Interface for Exploratory Volume Visualization
IEEE Transactions on Visualization and Computer Graphics
A Parallel Visualization Pipeline for Terascale Earthquake Simulations
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Gaussian Transfer Functions for Multi-Field Volume Visualization
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Querying for Feature Extraction and Visualization in Climate Modeling
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Data Parallel Bin-Based Indexing for Answering Queries on Multi-core Architectures
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Terascale data organization for discovering multivariate climatic trends
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Visualization for the Physical Sciences
Computer Graphics Forum
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Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets.