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Digital fabrication devices exploit basic technologies in order to create tangible reproductions of 3D digital models. Although current 3D printing pipelines still suffer from several restrictions, accuracy in reproduction has reached an excellent level. The manufacturing industry has been the main domain of 3D printing applications over the last decade. Digital fabrication techniques have also been demonstrated to be effective in many other contexts, including the consumer domain. The Cultural Heritage is one of the new application contexts and is an ideal domain to test the flexibility and quality of this new technology. This survey overviews the various fabrication technologies, discussing their strengths, limitations and costs. Various successful uses of 3D printing in the Cultural Heritage are analysed, which should also be useful for other application contexts. We review works that have attempted to extend fabrication technologies in order to deal with the specific issues in the use of digital fabrication in the Cultural Heritage. Finally, we also propose areas for future research.

The photorealistic acquisition of 3D objects often requires color information from digital photography to be mapped on the acquired geometry, in order to obtain a textured 3D model. This paper presents a novel fully automatic 2D/3D global registration pipeline consisting of several stages that simultaneously register the input image set on the corresponding 3D object. The first stage exploits Structure From Motion (SFM) on the image set in order to generate a sparse point cloud. During the second stage, this point cloud is aligned to the 3D object using an extension of the 4 Point Congruent Set (4PCS) algorithm for the alignment of range maps. The extension accounts for models with different scales and unknown regions of overlap. In the last processing stage a global refinement algorithm based on mutual information optimizes the color projection of the aligned photos on the 3D object, in order to obtain high quality textures. The proposed registration pipeline is general, capable of dealing with small and big objects of any shape, and robust. We present results from six real cases, evaluating the quality of the final colors mapped onto the 3D object. A comparison with a ground truth dataset is also presented

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This work concerns a novel study in the field of image-to-geometry registration. Our approach takes inspiration from medical imaging, in particular from multi-modal image registration. Most of the algorithms developed in this domain, where the images to register come from different sensors (CT, X-ray, PET), are based on Mutual Information, a statistical measure of non-linear correlation between two data sources. The main idea is to use mutual information as a similarity measure between the image to be registered and renderings of the model geometry, in order to drive the registration in an iterative optimization framework. We demonstrate that some illuminationrelated geometric properties, such as surface normals, ambient occlusion and reflection directions can be used for this purpose. After a comprehensive analysis of such properties we propose a way to combine these sources of information in order to improve the performance of our automatic registration algorithm. The proposed approach can robustly cover a wide range of real cases and can be easily extended.