Proceedings

Double Bounded Neural Ray Queries

Alexander Nikolaev, Nikolay Mozokhin, Roman Rodionov, Vladimir Frolov

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Demo Track. Pages 8475-8478.

https://doi.org/10.24963/ijcai.2026/983

Keywords

  • AI: Machine Learning

Abstract

We introduce a novel neural ray tracing method designed for compact scene representations and real-time rendering. To compress the scene with minimal fidelity losses, we address the issue of limiting the search space for intersections. For each scene we first construct two lightweight proxy shells that tightly bound the original surface from inside and outside. While executing ray queries, we intersect the rays with the shells and extract the regions that potentially contain the intersection with the original surface. Extracted regions are passed to the small neural network to retrieve the exact intersection location. We implement our method as part of a GPU-accelerated hybrid path tracing pipeline. We demonstrate it running real-time rendering on a variety of scenes, achieving up to 300x memory reduction and surpassing existing compressed ray tracing techniques in memory-quality trade-off.

Alexander Nikolaev, Nikolay Mozokhin, Roman Rodionov, and Vladimir Frolov. 2026. Double Bounded Neural Ray Queries. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 8475–8478. https://doi.org/10.24963/ijcai.2026/983
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