Hybrid Voxel Formats for Efficient Ray Tracing

TL;DR

This paper introduces hybrid voxel formats that combine multiple data structures hierarchically to optimize the trade-off between memory usage and ray tracing speed, demonstrating superior performance over single-format approaches.

Contribution

It proposes a systematic formulation of hybrid voxel formats with a metaprogramming system for automatic code generation and evaluation of their performance trade-offs.

Findings

Hybrid formats achieve Pareto optimal trade-offs.

Hybrid formats outperform standalone formats in speed and compression.

Transformations improve performance further.

Abstract

Voxels are a geometric representation used for rendering volumes, multi-resolution models, and indirect lighting effects. Since the memory consumption of uncompressed voxel volumes scales cubically with resolution, past works have introduced data structures for exploiting spatial sparsity and homogeneity to compress volumes and accelerate ray tracing. However, these works don't systematically evaluate the trade-off between compression and ray intersection performance for a variety of storage formats. We show that a hierarchical combination of voxel formats can achieve Pareto optimal trade-offs between memory consumption and rendering speed. We present a formulation of "hybrid" voxel formats, where each level of a hierarchical format can have a different structure. For evaluation, we implement a metaprogramming system to automatically generate construction and ray intersection code for arbitrary hybrid formats. We also identify transformations on these formats that can improve compression and rendering performance. We evaluate this system with several models and hybrid formats, demonstrating that compared to standalone base formats, hybrid formats achieve a new Pareto frontier in ray intersection performance and storage cost.