Adaptive Tetrahedral Grids for Volumetric Path-Tracing

TL;DR

This paper introduces an adaptive tetrahedral grid structure for volumetric path tracing that significantly reduces memory usage and enhances rendering performance, enabling real-time rendering of complex assets on GPUs.

Contribution

The authors present a novel adaptive tetrahedral grid construction method and optimized GPU algorithms that outperform traditional regular grids in volumetric rendering tasks.

Findings

GPU implementation is up to 30 times faster than regular grids.

Real-time rendering of production assets achieved at 32 samples per pixel.

Adaptive grids reduce memory footprint while maintaining high-quality rendering.

Abstract

We advertise the use of tetrahedral grids constructed via the longest edge bisection algorithm for rendering volumetric data with path tracing. The key benefits of such grids is two-fold. First, they provide a highly adaptive space-partitioning representation that limits the memory footprint of volumetric assets. Second, each (tetrahedral) cell has exactly 4 neighbors within the volume (one per face of each tetrahedron) or less at boundaries. We leverage these properties to devise optimized algorithms and data-structures to compute and path-trace adaptive tetrahedral grids on the GPU. In practice, our GPU implementation outperforms regular grids by up to x30 and renders production assets in real time at 32 samples per pixel.