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首页> 外文会议>Atas do 23 Encontro Portugues de Computacao Grafica e Interacao >Non-homogeneous grids for CPU-GPU ray tracing
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Non-homogeneous grids for CPU-GPU ray tracing

机译：用于CPU-GPU光线跟踪的非均匀网格

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摘要

Ray tracing is among the most resource consuming methods for realistic image generation. Over the years, different acceleration structures have been proposed to reduce ray-object intersection queries since these dominate execution time. Regular grids are one of the most popular structures due to their simplicity and effectiveness. However, regular grid implementations are plagued by two major issues: underwhelming performance on irregular scenes with unbalanced triangle density and high memory consumption due to the many empty cells in sparsely populated scenes, typical of many game scenarios. We present a novel hybrid solution based on non-homogeneous rectilinear grids to improve ray tracing performance on uneven scene distributions. Additionally, we use hashing to get rid of empty cells. Non-homogeneous grids feature moveable split planes along the three axes unlike regular grids where split planes must be equidistant. Our approach performs serial construction tasks such as compression in the CPU and offloads the remaining data parallel tasks to the GPU. Using this acceleration structure we are able to render a wide range of scenes at high frame rates on commodity graphics hardware, from irregular density low polygon count models to regular density high polygon count scanned scenes with rapid construction times and a small memory footprint. For some test cases, our approach nearly doubles the frame rate of a regular grid at a similar resolution, while featuring low build times.
机译：光线追踪是用于生成真实图像的最消耗资源的方法之一。多年来，已经提出了不同的加速结构来减少射线对象相交查询，因为它们占执行时间的主导。规则网格由于其简单性和有效性而成为最受欢迎的结构之一。但是，常规的网格实现受到两个主要问题的困扰：由于人口稀疏的场景中存在许多空单元（在许多游戏场景中很常见），在三角密度不平衡且不规则场景上的表现不佳。我们提出了一种基于非均匀直线网格的新型混合解决方案，以提高在不均匀场景分布上的光线跟踪性能。另外，我们使用散列来摆脱空单元格。不均匀的网格沿三个轴具有可移动的拆分平面，这与常规网格不同，后者的拆分平面必须等距。我们的方法在CPU中执行串行构建任务（例如压缩），并将其余的并行数据任务卸载到GPU。使用这种加速结构，我们可以在商品图形硬件上以高帧速率渲染各种场景，从不规则密度的低多边形数模型到规则密度的高多边形数扫描场景，构建时间短，内存占用少。对于某些测试案例，我们的方法以相似的分辨率几乎将常规网格的帧速率提高了一倍，而构建时间却很短。

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