doi:10.1145/3306346.3323036engLagunas, ManuelMalpica, SandraSerrano, AnaGarces, ElenaGutierrez, DiegoMasia, BelenA similarity measure for material appearanceART-2019-111642We present a model to measure the similarity in appearance between di erent materials, which correlates with human similarity judgments. We rst create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.2019http://zaguan.unizar.es/record/7914510.1145/3306346.3323036http://zaguan.unizar.es/record/79145oai:zaguan.unizar.es:79145info:eu-repo/grantAgreement/EC/H2020/682080/EU/Intuitive editing of visual appearance from real-world datasets/CHAMELEONThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 682080-CHAMELEONinfo:eu-repo/grantAgreement/ES/MINECO/TIN2016-78753-Pinfo:eu-repo/grantAgreement/ES/MINECO/TIN2016-79710-PACM TRANSACTIONS ON GRAPHICS 38, 4 (2019), 135 [12 pp.]by-nc-ndhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccess