Adaptive GPU Ray Casting Based on Spectral Analysis
Abstract
GPU based ray casting has become a valuable tool for the visualization of medical image data. While the method produces high-quality images, its main drawback is the high computational load. We present a novel adaptive approach to speed up the rendering. In contrast to well established heuristic methods, we use the spectral decomposition of the transfer function and the dataset to derive a suitable sampling criterion. It is shown how this criterion can be efficiently incorporated into an adaptive ray casting algorithm. Two medical datasets, which each represent a typical, but different material distribution, are rendered using the proposed method. An analysis of the number of sample points per ray reveals that the new algorithm requires 50% to 80% less points compared to a non-adaptive method without any quality loss. We also show that the rendering speed of the GPU implementation is greatly increased with reference to the non-adaptive algorithm.
Keywords
Transfer Function Graphic Processing Unit Spectral Decomposition Volume Rendering Adaptive SamplingPreview
Unable to display preview. Download preview PDF.
References
- 1.Bergner, S., Moller, T., Weiskopf, D., Muraki, D.J.: A spectral analysis of function composition and its implications for sampling in direct volume visualization. IEEE Transactions on Visualization and Computer Graphics 12(5), 1353–1360 (2006)CrossRefGoogle Scholar
- 2.Crassin, C., Neyret, F., Lefebvre, S., Eisemann, E.: Gigavoxels: Ray-guided streaming for efficient and detailed voxel rendering. In: Proceedings of the 2009 Symposium on Interactive 3D Graphics and Games, pp. 15–22. ACM, New York (2009)CrossRefGoogle Scholar
- 3.Engel, K., Kraus, M., Ertl, T.: High-quality pre-integrated volume rendering using hardware-accelerated pixel shading. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Workshop on Graphics Hardware, p. 16. ACM, New York (2001)Google Scholar
- 4.Gobbetti, E., Marton, F., Iglesias Guitián, J.A.: A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets. The Visual Computer 24(7), 797–806 (2008)CrossRefGoogle Scholar
- 5.Kraus, M.: Direct volume visualization of geometrically unpleasant meshes (2003)Google Scholar
- 6.Kraus, M., Strengert, M., Klein, T., Ertl, T.: Adaptive sampling in three dimensions for volume rendering on GPUs. In: 2007 6th International Asia-Pacific Symposium on Visualization, APVIS 2007, pp. 113–120 (2007)Google Scholar
- 7.Kruger, J., Westermann, R.: Acceleration techniques for GPU-based volume rendering. In: IEEE Visualization, VIS 2003, pp. 287–292 (2003)Google Scholar
- 8.Ljung, P.: Adaptive sampling in single pass, GPU-based raycasting of multiresolution volumes. In: Proceedings Eurographics/IEEE Workshop on Volume Graphics 2006, pp. 39–46 (2006)Google Scholar
- 9.Marschner, S.R., Lobb, R.J.: An evaluation of reconstruction filters for volume rendering. In: Proceedings of the Conference on Visualization 1994, pp. 100–107. IEEE Computer Society Press, Los Alamitos (1994)CrossRefGoogle Scholar
- 10.Roettger, S., Guthe, S., Weiskopf, D., Ertl, T., Strasser, W.: Smart hardware-accelerated volume rendering. In: Proceedings of the Symposium on Data Visualisation 2003, Eurographics Association, p. 238 (2003)Google Scholar
- 11.Rubin, G.D., Beaulieu, C.F., Argiro, V., Ringl, H., Norbash, A.M., Feller, J.F., Dake, M.D., Jeffrey, R.B., Napel, S.: Perspective volume rendering of CT and MR images: applications for endoscopic imaging. Radiology 199(2), 321 (1996)Google Scholar
- 12.Schulze, J.P., Kraus, M., Lang, U., Ertl, T.: Integrating pre-integration into the shear-warp algorithm. In: Proceedings of the 2003 Eurographics/IEEE TVCG Workshop on Volume Graphics, p. 118. ACM, New York (2003)Google Scholar