“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

Conference:


Entry Number: 33

Title:

    A Data-Driven Compression Method for Transient Rendering

Presenter(s):



Abstract:


    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.

References:


    • Qi Guo, Iuri Frosio, Orazio Gallo, Todd Zickler, and Jan Kautz. 2018. Tackling 3D ToF Artifacts through Learning and the FLAT Dataset. In the European Conference on Computer Vision (ECCV). 
    • Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE CVPR. 770–778. 
    • Adrian Jarabo, Julio Marco, Adolfo Munoz, Raul Buisan, Wojciech Jarosz, and Diego ˜ Gutierrez. 2014. A Framework for Transient Rendering. ACM Trans. Graph. 33, 6 (2014). 
    • Adrian Jarabo, Belen Masia, Julio Marco, and Diego Gutierrez. 2017. Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective. Visual Informatics 1, 1 (2017). Xiaochun Liu, Ibon Guillen, Marco La Manna, Ji Hyun Nam, Syed Azer Reza, Toan HuuLe, Adrian Jarabo, Diego Gutierrez, and Andreas Velten. 2019. Non-Line-of-Sight Imaging using Phasor Fields Virtual Wave Optics. Nature (2019). 
    • Julio Marco, Ibon Guill ´ en, Wojciech Jarosz, Diego Gutierrez, and Adrian Jarabo. 2019. ´ Progressive Transient Photon Beams. Computer Graphics Forum (2019). 
    • Julio Marco, Quercus Hernandez, Adolfo Munoz, Yue Dong, Adrian Jarabo, Min Kim, ‎ Xin Tong, and Diego Gutierrez. 2017. DeepToF: Off-the-Shelf Real-Time Correction of Multipath Interference in Time-of-Flight Imaging. ACM Trans. Graph. 36, 6, Article 219 (2017). 
    • Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning. ACM, 1096–1103

Keyword(s):



Acknowledgements:


    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).


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