“The role of objective and subjective measures in material similarity learning” by Delanoy, Lagunas, Galve, Gutierrez, Serrano, et al. …

  • ©Johanna Delanoy, Manuel Lagunas, Ignacio Galve, Diego Gutierrez, Ana Serrano, Roland Fleming, and Belen Masia

  • ©Johanna Delanoy, Manuel Lagunas, Ignacio Galve, Diego Gutierrez, Ana Serrano, Roland Fleming, and Belen Masia

  • ©Johanna Delanoy, Manuel Lagunas, Ignacio Galve, Diego Gutierrez, Ana Serrano, Roland Fleming, and Belen Masia

Conference:


Entry Number: 51

Title:

    The role of objective and subjective measures in material similarity learning

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Abstract:


    Establishing a robust measure for material similarity that correlates well with human perception is a long-standing problem. A recent work presented a deep learning model trained to produce a feature space that aligns with human perception by gathering human subjective measures. The resulting metric outperforms objective existing ones. In this work, we aim to understand whether this increased performance is a result of using human perceptual data or is due to the nature of feature learnt by deep learning models. We train similar networks with objective measures (BRDF similarity or classification task) and show that these networks can predict human judgements as well, suggesting that the non-linear features learnt by convolutional network might be a key to model material perception.

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Acknowledgements:


    This research has been partially funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (CHAMELEON project, grant agreement No 682080), the Spanish Ministry of Economy and Competitiveness (project TIN2016-79710-P) and the German Research Foundation (project 222641018-SFB/TRR 135 TP C1)


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