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- W4297462890 abstract "We propose neural global illumination, a novel method for fast rendering full global illumination in static scenes with dynamic viewpoint and area lighting. The key idea of our method is to utilize a deep rendering network to model the complex mapping from each shading point to global illumination. To efficiently learn the mapping, we propose a neural-network-friendly input representation including attributes of each shading point, viewpoint information, and a combinational lighting representation that enables high-quality fitting with a compact neural network. To synthesize high-frequency global illumination effects, we transform the low-dimension input to higher-dimension space by positional encoding and model the rendering network as a deep fully-connected network. Besides, we feed a screen-space neural buffer to our rendering network to share global information between objects in the screen-space to each shading point. We have demonstrated our neural global illumination method in rendering a wide variety of scenes exhibiting complex and all-frequency global illumination effects such as multiple-bounce glossy interreflection, color bleeding, and caustics." @default.
- W4297462890 created "2022-09-29" @default.
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- W4297462890 date "2022-01-01" @default.
- W4297462890 modified "2023-10-16" @default.
- W4297462890 title "Neural Global Illumination: Interactive Indirect Illumination Prediction under Dynamic Area Lights" @default.
- W4297462890 doi "https://doi.org/10.1109/tvcg.2022.3209963" @default.
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