“Deep Real-time Volumetric Rendering Using Multi-feature Fusion” by Hu, Yu, Liu, Yan, Wu, et al. …

  • ©Jinkai Hu, Chengzhong Yu, Hongli Liu, Lingqi Yan, Yiqian Wu, and Xiaogang Jin




    Deep Real-time Volumetric Rendering Using Multi-feature Fusion

Session/Category Title: Real-time Rendering: Gotta Go Fast!




    We present Multi-feature Radiance-Predicting Neural Networks (MRPNN), a practical framework with a lightweight feature fusion neural network for rendering high-order scattered radiance of participating media in real time. By reformulating the Radiative Transfer Equation (RTE) through theoretical examination, we propose transmittance fields, generated at a low cost, as auxiliary information to help the network better approximate the RTE, drastically reducing the size of the neural network. The light weight network efficiently estimates the difficult-to-solve in-scattering term and allows for configurable shading parameters while improving prediction accuracy. In addition, we propose a frequency-sensitive stencil design in order to handle non-cloud shapes, resulting in accurate shadow boundaries. Results show that our MRPNN is able to synthesize indistinguishable output compared to the ground truth. Most importantly, MRPNN achieves a speedup of two orders of magnitude compared to the state-of-the-art, and is able to render high-quality participating material in real time.


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