Fast Voxelization and Level of Detail for Microgeometry Rendering

2026-04-14

AI Summary

This work addresses the challenge of efficiently rendering sparse microstructured materials—such as fibrous or brushed metals—in volume rendering, which typically demand high-resolution representations yet incur substantial computational costs with conventional voxelization and multiscale rendering approaches. To overcome this, the authors propose an efficient parallel voxelization method that integrates hierarchical SGGX clustering to construct a level-of-detail (LoD) representation, significantly accelerating multiscale data aggregation and rendering. Implemented in CUDA, the approach supports both triangle meshes and explicit fiber models and is embedded within an SGGX distribution–driven LoD path tracing framework. Experimental results demonstrate that the method achieves a superior balance between rendering quality and performance across a range of microstructured materials compared to baseline techniques.

Technology Category

• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Matrix & Tensor Methods

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• voxelization
• Level of Detail
• microgeometry
• anisotropic scattering
• rendering

Innovation

• fast voxelization
• level of detail
• hierarchical SGGX clustering
• microgeometry rendering
• anisotropic scattering