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Efficient Sparse Voxel Octrees

机译:高效的稀疏体素八进制

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摘要

In this paper, we examine the possibilities of using voxel representations as a generic way for expressing complex and feature-rich geometry on current and future GPUs. We present in detail a compact data structure for storing voxels and an efficient algorithm for performing ray casts using this structure. We augment the voxel data with novel contour information that increases geometric resolution, allows more compact encoding of smooth surfaces, and accelerates ray casts. We also employ a novel normal compression format for storing high-precision object-space normals. Finally, we present a variable-radius postprocess filtering technique for smoothing out blockiness caused by discrete sampling of shading attributes. Based on benchmark results, we show that our voxel representation is competitive with triangle-based representations in terms of ray casting performance, while allowing tremendously greater geometric detail and unique shading information for every voxel. Our voxel codebase is open sourced and available at http://code.google.com/p/efficient-sparse-voxel-octrees/.
机译:在本文中,我们研究了使用体素表示作为在当前和将来的GPU上表达复杂且功能丰富的几何图形的通用方法的可能性。我们详细介绍了用于存储体素的紧凑数据结构以及使用该结构执行射线投射的有效算法。我们使用新颖的轮廓信息来增强体素数据,这些轮廓信息可提高几何分辨率,允许对平滑表面进行更紧凑的编码并加速射线投射。我们还采用了一种新颖的法线压缩格式来存储高精度的对象空间法线。最后,我们提出了一种可变半径的后处理滤波技术,用于消除由阴影属性的离散采样导致的块状性。根据基准测试结果,我们表明,在射线投射性能方面,我们的体素表示法与基于三角形的表示法具有竞争性,同时为每个体素提供了更大的几何细节和独特的着色信息。我们的体素代码库是开源的,可以在http://code.google.com/p/efficiency-sparse-voxel-octrees/上获得。

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