Renderizado Neuronal de Luminarias Complejas.

In this Master Thesis, we propose an efficient method for rendering complex luminaires based on neural networks. We reduce the geometric complexity of the luminaires by using a simple proxy geometry, and encode the visually-complex emitted light field by using a neural radiance field (NeRF). We tackle the multiple challenges of using NeRFs for representing luminaires, including their extreme dynamic range, their high-frequency content on the spatio temporal domain, and the spherical coverage, as well as the required modifications for seamlessly integrating our NeRF in synthetic enviroments. For that, we use a combination of non-exponential transmittance functions, and a novel loss that accounts for the HDR content as well as alpha blending for integration. We implement our model into a modern deep learning framework, and demonstrate high-quality neural rendering of such luminaires. Then, we integrate our model into the rendering software Mitsuba, and demonstrate renders with much less variance with a given sample count, simultaneously achieving a high visual quality. Finally, we propose several avenues for future work where our neural implicit luminaires could be used for importance sampling and drastically reduce rendering times.