A multiresolution framework for volume rendering
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SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
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VIS '97 Proceedings of the 8th conference on Visualization '97
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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A fast volume rendering algorithm for time-varying fields using a time-space partitioning (TSP) tree
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Accelerating time-varying hardware volume rendering using TSP trees and color-based error metrics
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IEEE Transactions on Visualization and Computer Graphics
Parallel Volume Rendering Using Binary-Swap Compositing
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PG '99 Proceedings of the 7th Pacific Conference on Computer Graphics and Applications
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VIS '95 Proceedings of the 6th conference on Visualization '95
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VIS '95 Proceedings of the 6th conference on Visualization '95
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VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
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EG PGV'04 Proceedings of the 5th Eurographics conference on Parallel Graphics and Visualization
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ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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We present a new parallel multiresolution volume rendering framework for large-scale time-varying data visualization using the wavelet-based time-space partitioning (WTSP) tree. Utilizing the wavelet transform, a largescale time-varying data set is converted into a space-time multiresolution data hierarchy, and is stored in a timespace partitioning (TSP) tree. To eliminate the parent-child data dependency for reconstruction and achieve loadbalanced rendering, we design an algorithm to partition the WTSP tree and distribute the wavelet-compressed data along hierarchical space-filling curves with error-guided bucketization. At run time, the WTSP tree is traversed according to the user-specified time step and tolerances of both spatial and temporal errors. Data blocks of different spatio-temporal resolutions are reconstructed and rendered to compose the final image in parallel. We demonstrate that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of time-varying data on a PC cluster.