Volume and Isosurface Rendering with GPU‐Accelerated Cell Projection*

Marroquim, Ricardo; Maximo, André; Farias, Ricardo; Esperança, Cláudio

doi:10.1111/j.1467-8659.2007.01038.x

Cited by 19 publications

(9 citation statements)

References 36 publications

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“… HAPT – Our approach, Hardware‐Assisted Projected Tetrahedra, with four different sorting methods; HAVS – Hardware‐Assisted Visibility Sorting [CICS05] with two k‐buffer sizes; PTINT – Projected Tetrahedra with Partial Pre‐Integration [MMFE08]; GATOR – GPU Accelerated Tetrahedra Renderer [WMFC02]; HARC – Hardware‐Based Ray Casting with normal pre‐integration [WKME03] and partial pre‐integration [EC05]. …”

Section: Resultsmentioning

confidence: 99%

“…Cell projection is a simple and efficient method widely used for direct volume rendering of irregular meshes [WMFC02, KQE04, SCT06, MMFE06, MMFE07, MMFE08]. One of the most popular cell‐projection technique is the Projected Tetrahedra (PT) algorithm introduced by Shirley and Tuchman [ST90].…”

Section: Related Workmentioning

confidence: 99%

“…Marroquim et al . [MMFE06,MMFE08] further improved the GATOR method by removing the redundancy of their basis graph and adding a better approximation of the final color. Their algorithm, called Projected Tetrahedra with Partial Pre‐Integration (PTINT), relies on a two‐pass GPU approach.…”

Section: Related Workmentioning

confidence: 99%

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Hardware‐Assisted Projected Tetrahedra

Maximo

Marroquim

Farias

2010

Computer Graphics Forum

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We present a flexible and highly efficient hardware-assisted volume renderer grounded on the original Projected Tetrahedra (PT) algorithm. Unlike recent similar approaches, our method is exclusively based on the rasterization of simple geometric primitives and takes full advantage of graphics hardware. Both vertex and geometry shaders are used to compute the tetrahedral projection, while the volume ray integral is evaluated in a fragment shader; hence, volume rendering is performed entirely on the GPU within a single pass through the pipeline. We apply a CUDA-based visibility ordering achieving rendering and sorting performance of over 6 M Tet/s for unstructured datasets. Furthermore, as each tetrahedron is processed independently, we employ a data-parallel solution which is neither bound by GPU memory size nor does it rely on auxiliary volume information. In addition, iso-surfaces can be readily extracted during the rendering process, and time-varying data are handled without extra burden.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Hardware‐Assisted Projected Tetrahedra

Maximo

Marroquim

Farias

2010

Computer Graphics Forum

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“…Rendering tetrahedral meshes is normally done using methods such as cell projection or custom ray-casting in which rays traverse through the tetrahedral mesh [7], [8], [9]. However, compared to regular-grid volume ray-casting, this method is slow, for example Marroquim et al [10] report only about 2.3 million tetrahedra per second on modern graphics hardware. While smaller data sets such as NASA's bluntfin data set only contain 160K tetrahedra, the plasma simulation data set provided to us by our Princeton Plasma Physics Laboratory (PPPL) collaborators contains 186M cells which, if tetrahedralized, would contain more than 928M tetrahedra.…”

Section: Related Workmentioning

confidence: 99%

Volume Rendering of Curvilinear-Grid Data Using Low-Dimensional Deformation Textures

Hero

2014

IEEE Trans. Visual. Comput. Graphics

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In this paper, we present a high quality and interactive method for volume rendering curvilinear-grid data sets. This method is based on a two-stage parallel transformation of the sample position into intermediate computational space then into texture space through the use of multiple 1 and 2D deformation textures using hardware acceleration. In this manner, it is possible to render many curvilinear-grid volume data sets at high quality and with a low memory footprint, while taking advantage of modern graphic hardware's tri-linear filtering for the data itself. We also extend our method to handle volume shading. Additionally, we present a comprehensive study and comparisons with previous works, we show improvements both in quality and performance using our technique on multiple curvilinear data sets.

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“…Significant research efforts have attempted to tackle these challenges using massively parallel graphics processors (GPU) [1], [2], [3], [4] and cluster-based parallelism [5], [6], [7], [8]. These studies take advantage of the continuing improvements in CPU and GPU performances as well as increasing multicore cluster-based parallelism.…”

Section: Introductionmentioning

confidence: 99%

Irregular Grid Raycasting Implementation on the Cell Broadband Engine

Cox

Maximo

Bentes

et al. 2009

2009 21st International Symposium on Computer Architecture and High Performance Computing

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Direct volume rendering has become a popular technique for visualizing volumetric data from sources such as scientific simulations, analytic functions, medical scanners, among others. Volume rendering algorithms, such as raycasting, can produce high-quality images, however, the use of raycasting has been limited due to its high demands on computational power and memory bandwidth. In this paper, we propose a new implementation of the raycasting algorithm that takes advantage of the highly parallel architecture of the Cell Broadband Engine processor, with 9 heterogeneous cores, in order to allow efficient raycasting of irregular datasets. All the computational power of the Cell BE processor, though, comes at the cost of a different programming model. Applications need to be rewritten, which requires using multithreading and vectorized code. In our approach, we tackle this problem by distributing ray computations using the visible faces, and vectorizing the lighting integral operations inside each core. Our experimental results show that we can obtain good speedups reducing the overall rendering time significantly.

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Volume and Isosurface Rendering with GPU‐Accelerated Cell Projection*

Cited by 19 publications

References 36 publications

Hardware‐Assisted Projected Tetrahedra

Hardware‐Assisted Projected Tetrahedra

Volume Rendering of Curvilinear-Grid Data Using Low-Dimensional Deformation Textures

Irregular Grid Raycasting Implementation on the Cell Broadband Engine

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