works, such as the PANORAMA views of the 3D objects [@Papadakis2010], the meshHOG [@meshHOG] and the meshLBP [@WerghiTBB16; @WerghiTBB15]. In general, the methods for matching textured 3D shapes adopt a combination of geometric and colorimetric descriptors. Possible choices of the colorimetric descriptors are: feature-vectors, where the color is treated as a general property of the shape, [@Suzuki01], or its subparts in [@garro16]; local or global views of the objects [@WuCLFP08; @Pasqualotto2013]; point-to-point correspondences among sets of feature points (e.g., the CSHOT descriptor [@TombariSS11]); the evolution of the sub-level sets according to the persistent homology settings [@PHOG]. These methods mainly address the shape matching problem without focusing on the surface details and local colorimetric variations. On the contrary, when looking for patterns, locality and scale are the two key aspects. A detailed evaluation and comparison of methods for 3D texture retrieval and comparison can be found in [@Biasotti2016] and several SHREC contests [@Cerri13; @Cerri14; @Giachetti15]. However, all these contests focused on the joint comparison of geometry and texture, without considering the comparison of the purely colorimetric information that characterizes the surface decorations.

At the best of our knowledge, the Mesh Local Binary Pattern (meshLBP) approach [@WerghiTBB16; @WerghiTBB15; @WerghiBB15] is the unique 