Fast Voxelization and Level of Detail for Microgeometry Rendering
It addresses the time and memory-intensive task of voxelizing sparse microstructures (e.g., fibers, brushed metal) for rendering, enabling faster data aggregation and improved LoD accuracy.
This work introduces an efficient parallel voxelization method for microgeometry and a novel hierarchical SGGX clustering representation for level-of-detail rendering, achieving better accuracy than baseline methods with a CUDA-based implementation tested on triangle meshes and volumetric fabrics.
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.