基于流的大数据集体绘制技术[j]

Yasuhiro Watashiba, J. Nonaka, Naohisa Sakamoto, Yasuo Ebara, K. Koyamada, M. Kanazawa
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引用次数: 2

摘要

本文提出了一种基于流的体绘制技术,该技术将大量的体数据分解成小块(子卷),以便在考虑系统可用资源的情况下在物理内存中保持体绘制处理。每个子卷被传送到渲染PC上,PC执行硬件加速的体渲染并生成部分图像(子图像)。子图像被深度堆叠,以完成最终图像。在渲染PC中,我们使用了通用消费PC显卡(Geforce4)作为大规模体渲染的低成本解决方案。此外,为了提高渲染质量,我们不使用平面切片采样,而是使用等距离表面切片采样。将该技术应用于侧囊性动脉瘤三维血流三维有限元分析结果的远程可视化,验证了该技术的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Streaming-based Technique for Volume Rendering of Large Datasets6
We present a streaming-based technique of volume rendering which breaks a large volume data into pieces (sub-volumes) so as to maintain volume rendering processing in physical memory in consideration of the available system resources. Each sub-volume is transferred to a rendering PC, which performs hardware accelerated volume rendering and generates a partial image (sub-image). The sub-images are piled up in depth order to complete the final image. In the rendering PC, we have used general-purpose consumer PC graphics cards (Geforce4) to our system as a low cost solution for large-scale volume rendering. Furthermore, in order to improve the rendering quality, we use not a plane slice sampling but an iso-distance surface slice sampling. This technique has been applied to remote visualization of 3-D finite element analysis result of 3-D flow through a lateral saccular aneurysm, and its effectiveness is confirmed.
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