The role of objective and subjective measures in material similarity learning

Johanna Delanoy, Manuel Lagunas, Ignacio Galve, Diego Gutierrez, Ana Serrano, Roland W. Fleming, Belén Masiá

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.