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Efficient deep Neural Network Architectures for Subsurface Scattering Approximation

Subsurface rendering has become essential for photo-realistic rendering. Therefore, an accurate simulation is too costly due to the interactions between the particles composing the material and the amount of light passing through it. In this paper, we propose a method to estimate the subsurface light scattering effects. Our method comprises two steps: the first step consists of generating synthetic images using a reference technique to train the neural network. The second step consists in generating an approximation of the reference image after a learning step. Our model is based on convolutional architecture. The neural network is fed in input by images retrieved from the G-buffer(depth map, normal map,albedo map), while in output, it generates an approximation of the subsurface diffusion. We show that our method produces effective approximations of subsurface scattering. Furthermore, our method generates images comparable to the reference method and does not require processing of the output image. Therefore, it does not do any calculation in the three-dimensional space of 3D scene.