LSNIF: Locally-Subdivided Neural Intersection Function

Shin Fujieda; Chih-Chen Kao; Takahiro Harada

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arXiv · 2504.21627

LSNIF: Locally-Subdivided Neural Intersection Function

Shin Fujieda·Chih-Chen Kao·Takahiro Harada

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Abstract

Neural representations have shown the potential to accelerate ray casting in a conventional ray-tracing-based rendering pipeline. We introduce a novel approach called Locally-Subdivided Neural Intersection Function (LSNIF) that replaces bottom-level BVHs used as traditional geometric representations with a neural network. Our method introduces a sparse hash grid encoding scheme incorporating geometry voxelization, a scene-agnostic training data collection, and a tailored loss function. It enables the network to output not only visibility but also hit-point information and material indices. LSNIF can be trained offline for a single object, allowing us to use LSNIF as a replacement for its corresponding BVH. With these designs, the network can handle hit-point queries from any arbitrary viewpoint, supporting all types of rays in the rendering pipeline. We demonstrate that LSNIF can render a variety of scenes, including real-world scenes designed for other path tracers, while achieving a memory footprint reduction of up to 106.2x compared to a compressed BVH.

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BibTeX RIS

Shin Fujieda, Chih-Chen Kao, Takahiro Harada. 2025-04-30. LSNIF: Locally-Subdivided Neural Intersection Function. https://arxiv.org/abs/2504.21627

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