SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
A polygonal approximation to direct scalar volume rendering
VVS '90 Proceedings of the 1990 workshop on Volume visualization
PRS '97 Proceedings of the IEEE symposium on Parallel rendering
A rendering algorithm for visualizing 3D scalar fields
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Fast Volume Rendering Using a Shear-Warp Factorization of the Viewing Transformation
Fast Volume Rendering Using a Shear-Warp Factorization of the Viewing Transformation
Acceleration Techniques for GPU-based Volume Rendering
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Large volume visualization of compressed time-dependent datasets on GPU clusters
Parallel Computing - Parallel graphics and visualization
Massively parallel volume rendering using 2-3 swap image compositing
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures
IEEE Transactions on Visualization and Computer Graphics
Hierarchical visualization and compression of large volume datasets using GPU clusters
EG PGV'04 Proceedings of the 5th Eurographics conference on Parallel Graphics and Visualization
Hi-index | 0.00 |
The ever-increasing amounts of volume data require high-end parallel visualization methods to process this data interactively. To meet the demands, progamming on graphics cards offers an effective and fast approach to compute volume rendering methods due to the parallel architecture of today's graphics cards. In this paper, we introduce a volume ray casting method working in parallel which provides an interactive visualization. Since data can be processed independently, we managed to speed up the computation on the GPU by a peak factor of more than 400 compared to our sequential CPU version. The parallelization is realized by using the application programming interface CUDA.