Abstract
The ray traversal in GPU-based volume raycasting is usually implemented in a fragment shader, utilizing the hardware in a way that was not originally intended. New programming interfaces for GPU computing, such as CUDA or OpenCL, support a more general programming model and the use of additional device features, which are not accessible through traditional shader programming. In this paper we first compare fragment shader implementations of basic raycasting to implementations directly translated to CUDA kernels. Then we propose a new slab-based raycasting technique that is modeled specifically to use the additional device features to accelerate volume rendering. We conclude that new GPU computing approaches can only gain a small performance advantage when directly porting the basic raycasting algorithm. However, they can be beneficial through novel acceleration methods that use the hardware features not available to shader implementations.
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References
AMD: Stream Computing User Guide, 1.4-beta (2009)
Grimm, S., Bruckner, S., Kanitsar, A., Gröller, M.E.: A refined data addressing and processing scheme to accelerate volume raycasting. Computers & Graphics 28(5), 719–729 (2004)
Kainz, B., Grabner, M., Bornik, A., Hauswiesner, S., Muehl, J., Schmalstieg, D.: Ray casting of multiple volumetric datasets with polyhedral boundaries on manycore GPUs. ACM Transactions on Graphics 28(5), 1–9 (2009)
Kim, J.: Efficient Rendering of Large 3-D and 4-D Scalar Fields. Ph.D. thesis, University of Maryland, College Park (2008)
Krüger, J., Westermann, R.: Acceleration techniques for GPU-based volume rendering. In: Proceedings of IEEE Visualization, pp. 287–292 (2003)
Law, A., Yagel, R.: Multi-frame thrashless ray casting with advancing ray-front. In: Proceedings of Graphics Interfaces, pp. 70–77 (1996)
Levoy, M.: Efficient ray tracing of volume data. ACM Transactions on Graphics 9(3), 245–261 (1990)
Luebke, D., Parker, S.: Interactive ray tracing with CUDA. Presentation at NVISION Conference (2008)
Maršálek, L., Hauber, A., Slusallek, P.: High-speed volume ray casting with CUDA. In: IEEE Symposium on Interactive Ray Tracing, p. 185 (2008)
Meyer-Spradow, J., Ropinski, T., Mensmann, J., Hinrichs, K.: Voreen: A rapid-prototyping environment for ray-casting-based volume visualizations. IEEE Computer Graphics and Applications 29(6), 6–13 (2009)
Munshi, A. (ed.): The OpenCL Specification, Version 1.0. Khronos OpenCL Working Group (2008)
Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with CUDA. ACM Queue 6(2), 40–53 (2008)
Röttger, S., Guthe, S., Weiskopf, D., Ertl, T., Straßer, W.: Smart hardware-accelerated volume rendering. In: VISSYM 2003: Proceedings of the Symposium on Data Visualisation, pp. 231–238 (2003)
Scharsach, H., Hadwiger, M., Neubauer, A., Wolfsberger, S., Bühler, K.: Perspective isosurface and direct volume rendering for virtual endoscopy applications. In: Eurographics/IEEE VGTC Symposium on Visualization, pp. 315–322 (2006)
Smelyanskiy, M., Holmes, D., Chhugani, J., Larson, A., et al.: Mapping high-fidelity volume rendering for medical imaging to CPU, GPU and many-core architectures. IEEE Transactions on Visualization and Computer Graphics 15(6), 1563–1570 (2009)
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Mensmann, J., Ropinski, T., Hinrichs, K. (2011). Slab-Based Raycasting: Exploiting GPU Computing for Volume Visualization. In: Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Applications. VISIGRAPP 2010. Communications in Computer and Information Science, vol 229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25382-9_17
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DOI: https://doi.org/10.1007/978-3-642-25382-9_17
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