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Many computer vision techniques for 3D-reconstruction have been developped, building upon different clues. Most of them are based on triangulation, which provides the 3D-position of a point seen from at least two viewpoints. From theory to applications, these techniques have been widely studied, with undeniable success. However, even up-to-date techniques such as structure-from-motion or multi-view stereo reach their limits when faced to specular materials i.e., when a 3D-point does not look equally bright from different viewpoints. Another limit of triangulation is that it may provide the 3D-shape, but not the reflectance of a scene. These limitations have motivated recently a renewed interest in photometric 3D-reconstruction. Such techniques as shape-from-shading, photometric stereo, or shape-from-polarization rely on the relationship between the image color and the characteristics of the triplet scene-lighting-camera. In order to estimate geometric and photometric clues, their goal is to invert the image formation process. To this end, various mathematical tools can be employed, for instance variational methods, PDEs or machine learning. The field of photometric 3D-reconstruction has taken advantage of the complementarity of two research communities: mathematical imaging and computer vision. The aim of the proposed minisymposium is to bring together researchers from both these communities, in order to discuss the recent advances in the field.