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Adaptive sampling in three dimensions for volume rendering on GPUs

机译:三维自适应采样,可在GPU上进行体积渲染

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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
机译:可编程图形硬件上的大体积数据集的直接体积渲染通常受到可用图形内存的数量以及从主内存到图形内存的带宽的限制。因此,已经公开了从体积数据的紧凑表示来进行体积渲染的几种方法,这些方法避免了主存储器与图形编程单元(GPU)之间的大多数数据传输,但以GPU进行额外的数据解压缩为代价。为了降低这种性能成本,提出了自适应采样技术。但是,通常仅限于沿视图方向进行采样。在这项工作中,我们提出了一种基于GPU的体绘制算法,该算法在所有三个空间方向均具有自适应采样;即,不仅在观看方向上,而且在像平面的两个垂直方向上。这种方法使我们可以在不影响图像质量的情况下大幅减少样本数量。因此,它特别适合需要数据的基于计算的昂贵GPU采样的体积数据的许多压缩表示形式

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