“A Data-Driven Compression Method for Transient Rendering” by Gutierrez, Chen, Munoz, Huang, Marco, et al. …

  • ©Diego Gutierrez, Mingqin Chen, Adolfo Munoz, Zesheng Huang, Julio Marco, and Yun Liang

  • ©Diego Gutierrez, Mingqin Chen, Adolfo Munoz, Zesheng Huang, Julio Marco, and Yun Liang

  • ©Diego Gutierrez, Mingqin Chen, Adolfo Munoz, Zesheng Huang, Julio Marco, and Yun Liang

  • ©Diego Gutierrez, Mingqin Chen, Adolfo Munoz, Zesheng Huang, Julio Marco, and Yun Liang


Entry Number: 33


    A Data-Driven Compression Method for Transient Rendering



    Monte Carlo methods for transient rendering have become a powerful instrument to generate reliable data in transient imaging applications, either for benchmarking, analysis, or as a source for data-driven approaches. However, due to the increased dimensionality of time-resolved renders, storage and data bandwidth are significant limiting constraints, where a single time-resolved render of a scene can take several hundreds of megabytes. In this work we propose a learning-based approach that makes use of deep encoder decoder architectures to learn lower-dimensional feature vectors of time-resolved pixels. We demonstrate how our method is capable of compressing transient renders up to a factor of 32, and recover the full transient profile making use of a decoder. Additionally, we show how our learned features significantly mitigate variance on the recovered signal, addressing one of the pathological problems in transient rendering.


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    This is project was funded by DARPA (project REVEAL), the European Research Council under the EU’s Horizon 2020 research and innovation programme (project CHAMELEON, grant No. 682080), the Spanish Ministry of Economy and Competitiveness (project TIN2016-78753-P), the National Natural Science Fund of China (61772209), and the Science and Technology Planning Project of Guangdong Province (2016A050502050).