GPU accelerated image aligned splatting

N. Neophytou, K. Mueller
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引用次数: 59

Abstract

Splatting is a popular technique for volume rendering, where voxels are represented by Gaussian kernels, whose pre-integrated footprints are accumulated to form the image. Splatting has been mainly used to render pre-shaded volumes, which can result in significant blurring in zoomed views. This can be avoided in the image-aligned splatting scheme, where one accumulates kernel slices into equi-distant, parallel sheet buffers, followed by classification, shading, and compositing. In this work, we attempt to evolve this algorithm to the next level: GPU (graphics processing unit) based acceleration. First we describe the challenges that the highly parallel "Gather" architecture of modern GPUs poses to the "Scatter" based nature of a splatting algorithm. We then describe a number of strategies that exploit newly introduced features of the latest-generation hardware to address these limitations. Two crucial operations to boost the performance in image-aligned splatting are the early elimination of hidden splats and the skipping of empty buffer-space. We describe mechanisms which take advantage of the early z-culling hardware facilities to accomplish both of these operations efficiently in hardware.
GPU加速图像对齐飞溅
飞溅是一种流行的体绘制技术,其中体素由高斯核表示,其预集成足迹累积形成图像。飞溅主要用于渲染预阴影的体块,这会导致放大视图中明显的模糊。这可以在图像对齐的溅射方案中避免,在该方案中,将内核切片累积到等距、平行的片缓冲区中,然后进行分类、着色和合成。在这项工作中,我们试图将该算法发展到下一个层次:基于GPU(图形处理单元)的加速。首先,我们描述了现代gpu的高度并行“聚集”架构对飞溅算法的“散射”性质所带来的挑战。然后,我们描述了一些利用最新一代硬件新引入的特性来解决这些限制的策略。提高图像对齐溅射性能的两个关键操作是尽早消除隐藏的溅射和跳过空的缓冲空间。我们描述了利用早期z-culling硬件设施在硬件中高效完成这两个操作的机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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