Real-world comprises thousands of materials having different surface appearances. These appearances are a key in our everyday judgements of material properties, which are based on our past experience, recognition, and usage of these materials.
Although current technology allows for realistic reproduction of material appearance for visualization and quality control purposes, sharing of materials and their properties information across different measured representations and software platforms is still rather complicated. This problem relates to digital material appearance assessment over entire pipeline of its lifetime, i.e. from its acquisition, measured data modeling, to its visualization.
The goal of this project is to create material identifier encoding its perceptual visual features. These features obtained by judgements of observers in psychophysical studies form so called material fingerprint. Based on sparse material measurements and the qualities judgements, we plan to provide their generalization and propagation in a form of ontology of existing material appearances based on methods of adaptive machine learning. Such a system would assess regular measurements of any material appearance and provide its perceptual fingerprint allowing its efficient categorization, retrieval and sharing among users.