MULTIRESOLUTION RENDERING BASED ON GPGPU COMPUTING

Authors

  • Julián Lamas-Rodríguez
  • Francisco Argüello
  • Dora B. Heras

DOI:

https://doi.org/10.47839/ijc.12.4.609

Keywords:

Compressed volume rendering, texture mapping, multiresolution rendering, wavelet transform, quantization, CUDA, OpenGL.

Abstract

The problem of visualizing large volumetric datasets is appealing for computation on the GPU. Nevertheless, the design of GPU volume rendering solutions must deal with the limited available memory in a graphics card. In this work, we present a system for multiresolution volume rendering which preprocesses the dataset dividing it into bricks and generating a compressed version by applying different levels of compression based on wavelets. The compressed volume is then stored in the GPU memory. For the later visualization process by texture mapping each brick of the volume is decompressed and rendered with a different resolution level depending on its distance to the camera. This approach computes most of the tasks in the GPU, thus minimizing the data transfers among CPU and GPU. We obtain competitive results for volumes of size in the range between 64 and 256.

References

N. Fout and K.-L. Ma. Transform coding for hardware-acceleratedvolume rendering, IEEE Transactions on Visualization and Computer Graphics, (13) 6 (2007), pp. 1600-1607.

I. Ihm and S. Park, Wavelet-based 3D compression scheme for interactive visualization of very large volume data, Computer Graphics Forum, Lake Tahoe, CA, (18) 1 (May 2-5, 1999), pp. 3-15.

S. Guthe, M. Wand, J. Gonser, and W. Stra?er, Interactive rendering of large volume data sets, in IEEE Visualization 2002, Boston, Massachusetts, (Oct. 27-Nov. 1, 2002), pp. 53-60.

J. Schneider and R. Westermann, Compression domain volume rendering, in IEEE Visualization 2003, Seattle, Washington, (Oct. 19-24, 2003) pp. 293-300.

R. Parys and G. Knittel, Giga-voxel rendering from compressed data on a display wall, Journal of WSCG, (17) 13 (2009), pp. 73-80.

E. Gobbetti, J. A. Iglesias Guitian, and F. Marton, COVRA: A compression-domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks, Computer Graphics Forum, (31) 3-4 (2012), pp. 1315-1324.

S. K. Suter, J. A. Iglesias Guitian, F. Marton, M. Agus, A. Elsener, C. P. Zollikofer, M. Gopi, E. Gobbetti, and R. Pajarola. Interactive multiscale tensor reconstruction for multiresolution volume visualization, IEEE Transactions on Visualization and Computer Graphics, (17) 12 (2011), pp. 2135-2143.

M. B. Rodriguez, E. Gobbetti, J. I. Guitian, M. Makhinya, F. Marton, R. Pajarola, and S. Suter, A survey of compressed GPU-based direct volume rendering,” Eurographics 2013, Girona, Spain, (May 6-10, 2013), pp. 117-136.

Julian Lamas-Rodriguez, Francisco Arguello, Dora B. Heras, “A GPU-based Multiresolution Pipeline for Compressed Volume Rendering”, The 2013 International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, EEUU, (July 22-25, 2013), pp. 523-529.

A.V. Gelder and K. Kim, Direct volume rendering with shading via three-dimensional textures, 1996 Symposium on Volume Visualization, (Oct. 28-29, 1996), pp. 23-30.

K. Engel, M. Hadwiger, J. M. Kniss, C. Rezk-Salama, and D. Weiskopf, Real-time volume graphics. A K Peters, Ltd., 2006.

Q. Zhang, R. Eagleson, and T. M. Peters. Volume visualization: a technical overview with a focus on medical applications, Journal of Digital Imaging, (24) 4 (2011), pp. 640-664.

E. Gobbetti, F. Marton, and J. A. I. Guitian. A single-pass GPU raycasting framework for interactive out-of-core rendering of massive volumetric datasets, The Visual Computer, (24) 7–9, (2008), pp. 797-806.

B. Liu, G. J. Clapworthy, F. Dong, and E. C. Prakash. Octree rasterization: accelerating high-quality out-of-core GPU volume rendering, IEEE Transactions on Visualization and Computer Graphics, (99) (2012), pp. 1-14.

J. Nickolls and W. J. Dally. The GPU computing era, IEEE Micro, (30) 2 (2010), pp. 56-69.

CUDA C programming guide (version 4.0), NVIDIA, 2011.

D. B. Kirk and W.-m. W. Hwu, Programming massively parallel processors: a hands-on approach. Burlington, Massachusetts, USA: Elsevier, 2010.

R. M. Gray and D. L. Neuhoff, Quantization, IEEE Transactions on Information Theory, (44) 6 (1998), pp. 2325–2383.

C. Cocosco, V. Kollokian, R.-S. Kwan, and A. Evans, BrainWeb: online interface to a 3D MRI simulated brain database, NeuroImage, (5) 4 (1997), p. S425.

G. Bernabe, G. D. Guerrero, and J. Fernandez, CUDA and OpenCL implementations of 3D fast wavelet transform, 3rd IEEE Latin American Symposium on Circuits and Systems, Playa del Carmen, Mexico, (Feb. 29- March 2, 2012), pp. 1-4.

I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1992.

R. D. Dony, Karhunen-Loeve transform, in The Transform and Data Compression Handbook. Boca Raton, Florida, CRC Press, 2004.

K. I. Iourcha, K. S. Nayak, and Z. Hong, System and method for fixed-rate block-based image compression with inferred pixel values, US Patent 5 956 431, Sept. 21, 1999.

Y. Cao, L. Xiao, and H. Wang, “Hardware-accelerated volume rendering based on DXT compressed datasets,” International Conference on Audio, Language and Image Processing, Shangai, China, (Nov. 23-25, 2010), pp. 523-52.

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Published

2014-08-01

How to Cite

Lamas-Rodríguez, J., Argüello, F., & Heras, D. B. (2014). MULTIRESOLUTION RENDERING BASED ON GPGPU COMPUTING. International Journal of Computing, 12(4), 298-307. https://doi.org/10.47839/ijc.12.4.609

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Articles