PureLight: Learning Complex Luminaires with Light Tracing
We propose a neural formulation for estimating the appearance of complex luminaires. We focus on challenging luminaires with complex light transport (e.g., small emitters enclosed by multiple specular layers) that are difficult for (bidirectional) path tracing. To this end, we use light tracing to construct paths from emitters to the exit surfaces and formulate appearance estimation as a distribution learning problem. Specifically, we model the probability density function (pdf) of outgoing radiance on the exit surfaces using a large normalizing flow network, and recover the outgoing radiance as the product of the estimated pdf and flux. To enable efficient inference, we distill the learn...
| Authors | Pedro Figueiredo, Zixuan Li, Beibei Wang et al. |
|---|---|
| Primary category | cs.GR |
| Published | 3 June 2026 |
| Last updated at source | 16 September 2026 |
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