Fast Voxelization and Level of Detail for Microgeometry Rendering

TL;DR

This paper introduces a fast parallel voxelization method and a hierarchical SGGX clustering representation to improve microgeometry rendering efficiency and accuracy, validated through CUDA implementation and path tracing.

Contribution

It presents a novel, efficient parallel voxelization technique and a hierarchical SGGX clustering model for enhanced microgeometry rendering and level-of-detail management.

Findings

CUDA-based voxelizer achieves faster data processing.

Hierarchical SGGX clustering improves accuracy over baseline methods.

Path tracer results demonstrate effective LoD rendering of microstructures.

Abstract

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.