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Research Article | Open Access

Efficient participating media rendering with differentiable regularization

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Adobe Research, San Jose, CA 95110-2704, USA
Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, China
Department of Computer Science, University of California, Santa Barbara, CA 93106, USA
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Abstract

Highly scattering media, such as milk, skin, and clouds, are common in the real world. Rendering participating media is challenging, especially for high-order scattering dominant media, because the light may undergo a large number of scattering events before leaving the surface. Monte Carlo-based methods typically require a long time to produce noise-free results. Based on the observation that low-albedo media contain less noise than high-albedo media, we propose reducing the variance of the rendered results using differentiable regularization. We first render an image with low-albedo participating media together with the gradient with respect to the albedo, and then predict the final rendered image with a low-albedo image and gradient image via a novel prediction function. To achieve high quality, we also consider the gradients of neighboring frames to provide a noise-free gradient image. Ultimately, our method can produce results with much less overall error than equal-time path tracing methods.

Keywords

participating mediadifferentiable regularizationdifferentiable renderingvolumetric path tracingtemporal denoising

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Computational Visual Media
Volume 10 Issue 5,
October 2024
Pages 937-948
DOI: 10.1007/s41095-023-0372-2
Cite this article:
Wu W, Wang B, Hašan M, et al. Efficient participating media rendering with differentiable regularization. Computational Visual Media, 2024, 10(5): 937-948. https://doi.org/10.1007/s41095-023-0372-2
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