000112202 001__ 112202 000112202 005__ 20220510091956.0 000112202 037__ $$aTAZ-TFM-2022-035 000112202 041__ $$aeng 000112202 1001_ $$aCondor Lacambra, Jorge 000112202 24200 $$aNeural Rendering of Complex Luminaires. 000112202 24500 $$aRenderizado Neuronal de Luminarias Complejas. 000112202 260__ $$aZaragoza$$bUniversidad de Zaragoza$$c2022 000112202 506__ $$aby-nc-sa$$bCreative Commons$$c3.0$$uhttp://creativecommons.org/licenses/by-nc-sa/3.0/ 000112202 520__ $$aIn this Master Thesis, we propose an efficient method for rendering complex luminaires based on neural networks. We reduce the geometric complexity of the luminaires by using a simple proxy geometry, and encode the visually-complex emitted light field by using a neural radiance field (NeRF). We tackle the multiple challenges of using NeRFs for representing luminaires, including their extreme dynamic range, their high-frequency content on the spatio temporal domain, and the spherical coverage, as well as the required modifications for seamlessly integrating our NeRF in synthetic enviroments. For that, we use a combination of non-exponential transmittance functions, and a novel loss that accounts for the HDR content as well as alpha blending for integration. We implement our model into a modern deep learning framework, and demonstrate high-quality neural rendering of such luminaires. Then, we integrate our model into the rendering software Mitsuba, and demonstrate renders with much less variance with a given sample count, simultaneously achieving a high visual quality. Finally, we propose several avenues for future work where our neural implicit luminaires could be used for importance sampling and drastically reduce rendering times.<br /> 000112202 521__ $$aMáster Universitario en Robótica, Gráficos y Visión por Computador 000112202 540__ $$aDerechos regulados por licencia Creative Commons 000112202 700__ $$aJarabo Torrijos, Adrián$$edir. 000112202 7102_ $$aUniversidad de Zaragoza$$bInformática e Ingeniería de Sistemas$$cLenguajes y Sistemas Informáticos 000112202 8560_ $$f736052@unizar.es 000112202 8564_ $$s21586192$$uhttps://zaguan.unizar.es/record/112202/files/TAZ-TFM-2022-035.pdf$$yMemoria (eng) 000112202 909CO $$ooai:zaguan.unizar.es:112202$$pdriver$$ptrabajos-fin-master 000112202 950__ $$a 000112202 951__ $$adeposita:2022-05-10 000112202 980__ $$aTAZ$$bTFM$$cEINA 000112202 999__ $$a20220128212729.CREATION_DATE