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GPU Accelerated Isosurface Extraction on Tetrahedral Grids

机译:四面体网格上的GPU加速等值面提取

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摘要

Visualizing large unstructured grids is extremely useful to understand natural and simulated phenomena. However, informative volume visualization is difficult to achieve efficiently due to the huge amount of information to process. In this paper, we present a method to efficiently tessellate on a GPU large unstructured tetrahedral grids made of millions of cells. This method avoids data redundancy by using textures for storing most of the needed data; textures are accessed through vertex texture lookup in the vertex shading unit of modern graphics cards. Results show that our method is about 2 times faster than the same CPU-based extraction, and complementary with previous approaches based on GPU registers: it is less efficient for small grids, but handles millions-tetrahedra grids in graphics memory, which was impossible with previous works. Future hardware evolutions are expected to make our approach much more efficient.
机译:可视化大型非结构化网格对于理解自然现象和模拟现象非常有用。但是,由于要处理的信息量巨大,难以有效实现信息量的可视化。在本文中,我们提出了一种方法,可以在GPU上有效地细分由数百万个单元组成的大型非结构化四面体网格。此方法通过使用纹理存储大多数所需数据来避免数据冗余;通过现代图形卡的顶点着色单元中的顶点纹理查找可以访问纹理。结果表明,我们的方法比基于CPU的相同提取快大约2倍,并且与以前基于GPU寄存器的方法相辅相成:对于小型网格而言效率较低,但是可以处理图形内存中的数百万个四面体网格,而这在使用图形处理器时是不可能的。以前的作品。未来的硬件发展有望使我们的方法更加高效。

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