Real-Time Neural Homogeneous Translucent Material Rendering Using Diffusion Blocks.

Di An, Liangfu Kang, Kun Xu
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Abstract

Rendering realistic appearances of homogeneous translucent materials, such as milk and marble, poses challenges due to the complexity of subsurface scattering. In this paper, we present a neural method for real-time rendering of homogeneous translucent objects. Based on the observation that light propagation inside a highly scattered media is like a diffusion process [1], we propose a neural data structure named diffusion block to mimic the behavior of the diffusion process. The diffusion block is built upon a recent network structure named DiffusionNet [2] with a few modifications to adapt to our problem of translucent rendering. Our network is lightweight and efficient, leading to a real-time rendering method. Furthermore, our method supports dynamic material properties and diverse lighting conditions. Comparisons with state-of-the-art real-time translucent rendering methods demonstrate the superiority of our method in rendering quality.

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Real-Time Neural Homogeneous Translucent Material Rendering Using Diffusion Blocks. Retraction Notice: iMetaTown: A Metaverse System with Multiple Interactive Functions Based on Virtual Reality. Unsupervised Non-Rigid Human Point Cloud Registration Based on Deformation Field Fusion. The Role of Sensorimotor Contingencies and Eye Scanpath Entropy in Presence in Virtual Reality: a Reinforcement Learning Paradigm. GlossyGS: Inverse Rendering of Glossy Objects With 3D Gaussian Splatting.
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