Many materials show anisotropic light scattering patterns due to the shape and local alignment of their underlying micro structures: surfaces with small elements such as fibers, or the ridges of a brushed metal, are very sparse and require a high spatial resolution to be properly represented as a volume. The acquisition of voxel data from such objects is a time and memory-intensive task, and most rendering approaches require an additional Level-of-Detail (LoD) data structure to aggregate the visual appearance, as observed from multiple distances, in order to reduce the number of samples computed per pixel (E.g.: MIP mapping). In this work we introduce first, an efficient parallel voxelization method designed to facilitate fast data aggregation at multiple resolution levels, and second, a novel representation based on hierarchical SGGX clustering that provides better accuracy than baseline methods. We validate our approach with a CUDA-based implementation of the voxelizer, tested both on triangle meshes and volumetric fabrics modeled with explicit fibers. Finally, we show the results generated with a path tracer based on the proposed LoD rendering model.

翻译：许多材料因微观结构的形状与局部排列而呈现各向异性光散射模式：表面上的细小元素（如纤维）或拉丝金属的脊线非常稀疏，需要高空间分辨率才能以体素形式恰当表示。从这些物体中获取体素数据是耗时且消耗内存的任务，大多数渲染方法还需要额外的细节层次（LoD）数据结构来聚合从不同距离观察到的视觉外观，以减少每像素计算样本数量（例如：MIP映射）。本文首先提出一种高效的并行体素化方法，旨在促进多分辨率层级下的快速数据聚合；其次，提出一种基于分层SGGX聚类的全新表示方法，其精度优于基线方法。我们通过基于CUDA实现的体素化器验证了该方法，并分别在三角形网格和以显式纤维建模的体积织物上进行了测试。最后，我们展示了基于所提LoD渲染模型的光线追踪器生成的结果。