Deep Real-time Volumetric Rendering Using Multi-feature Fusion

Published in ACM SIGGRAPH 2023 Conference Proceedings (SIGGRAPH '23). Association for Computing Machinery, New York, NY, USA, Article 61, 1–10., 2023

Jinkai Hu, Chengzhong Yu, Hongli Liu, Ling-qi Yan, Yiqian Wu, Xiaogang Jin



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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