基于gpu的三维体绘制自适应采样

Martin Kraus, M. Strengert, T. Klein, T. Ertl
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引用次数: 1

摘要

在可编程图形硬件上对大容量数据集的直接体绘制通常受到可用图形存储器的数量和从主存储器到图形存储器的带宽的限制。因此,已经发布了几种从体积数据的紧凑表示进行体绘制的方法,这些方法避免了主存储器和图形编程单元(GPU)之间的大部分数据传输,但代价是GPU需要额外的数据解压。为了降低这种性能成本,提出了自适应采样技术;然而,这通常局限于视点方向的采样。在这项工作中,我们提出了一种基于gpu的体绘制算法,该算法在三个空间方向上都具有自适应采样;即,不仅在视图方向上,而且在成像平面的两个垂直方向上。这种方法使我们能够在不影响图像质量的情况下显著减少样本数量;因此,它特别适合于许多体积数据的压缩表示,这些数据需要基于gpu的数据采样,计算成本很高。
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Adaptive sampling in three dimensions for volume rendering on GPUs
Direct volume rendering of large volumetric data sets on programmable graphics hardware is often limited by the amount of available graphics memory and the bandwidth from main memory to graphics memory. Therefore, several approaches to volume rendering from compact representations of volumetric data have been published that avoid most of the data transfer between main memory and the graphics programming unit (GPU) at the cost of additional data decompression by the GPU. To reduce this performance cost, adaptive sampling techniques were proposed; which are, however, usually restricted to the sampling in view direction. In this work, we present a GPU-based volume rendering algorithm with adaptive sampling in all three spatial directions; i.e., not only in view direction but also in the two perpendicular directions of the image plane. This approach allows us to reduce the number of samples dramatically without compromising image quality; thus, it is particularly well suited for many compressed representations of volumetric data that require a computational expensive GPU-based sampling of data.
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