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Adaptive Tetrahedral Grids for Volumetric Path-Tracing

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Anis Benyoub, Jonathan Dupuy

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Adaptive Tetrahedral Grids for Volumetric Path-Tracing presents a SIGGRAPH Talks 2025 publication by Anis Benyoub and Jonathan Dupuy of Intel Corporation on the GGX Research site.

The work advertises longest-edge-bisection tetrahedral grids for path-tracing volumetric data. Benefits include adaptive space partitioning that limits memory for volumetric assets and a fixed neighbor count of four per interior tetrahedral cell. The authors describe GPU algorithms and data structures to build and path-trace these grids, reporting up to about 30x speedups versus regular grids and real-time rendering of production assets at 32 samples per pixel.

It is aimed at advanced researchers and rendering engineers working on volumetric path tracing and adaptive spatial structures. Familiarity with GPU path tracing is expected.

The page is a free project abstract with title, authors, venue, and a short abstract text. Full paper or talk media may be linked from the publication entry when available.

It is useful as a concise pointer to Intel research on adaptive tet grids that trade regular-grid cost for neighbor-friendly volumetric traversal. Link last verified accessible on 2026-09-29. Link last verified accessible on 2026-09-28.