DiffusionShadow: Diffusion-based Shadow Caching for Neural Volume Rendering
arXiv cs.GR details DiffusionShadow, a diffusion-based shadow caching framework for neural volume rendering. The core idea is to compress a large set of precomputed shadow implicit neural representations into a single diffusion model, then reconstruct the needed shadow INR weights on demand from the lighting direction.
That matters because real-time volume rendering with shadows is still expensive: ray-marched shadow terms cost time, while brute-force precomputation across many directions explodes storage. DiffusionShadow is aimed at that tradeoff, trying to preserve the look of dense lighting coverage without carrying a separate model for every direction.
The pipeline first encodes shadow coefficient volumes as shadow INRs, then trains a diffusion model to predict the corresponding INR weights at inference. Because it plugs into standard INR renderers and avoids extra runtime sampling, it should be easier to slot into existing neural volume rendering stacks than a more invasive renderer rewrite.
The authors say experiments show faster rendering than traditional approaches, with shadows that closely match most reference results. For graphics programmers and technical artists working on scientific visualization, volumetric tools, or neural rendering research, the practical appeal is clear: less storage bloat, lower runtime cost, and a path toward richer lighting on large datasets.
“compresses a vast set of pre-calculated shadow INRs into a single diffusion model”
- what
- DiffusionShadow compresses many shadow INRs into one diffusion model for neural volume rendering.
- who
- Authors: Kai-Chen Tung, Qi Wu, David Bauer, Mengjiao Han, Silvio Rizzi, and Kwan-Liu Ma.
- when
- Submitted to arXiv on 25 Sep 2026.
- impact
- Aims to reduce storage and runtime cost for shadowed volume rendering without extra sampling.
Promising speed/storage win for volumetric rendering
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