-This paper faces the problem of acquiring archaeological artifacts using triangulation based 3D laser scanners and focusing on reflective/refractive surfaces. This kind of artifacts are mostly made of glass or polished metal, and the properties of their surfaces violate most of the fundamental assumptions made by vision algorithms. Also, the unique and fragile nature of archaeological artifacts adds an extra limitation to the acquisition process: using industrial whitening sprays has to be avoided, due to the physicochemical processes required to clean the surface after scanning and because the chemical properties of these sprays may damage the original object. As an alternative to them, a new application of a common conservation material is proposed: the use of cyclododecane as a whitening spray. Thanks to its chemical stability and to the fact that it sublimes at room temperature, together with its good film-forming capabilities, a set of evaluation tests is presented to prove that the error introduced by the opaque thin layer created on the surface of the artifact is smaller than the accuracy of the 3D scanner and, thus, no acquisition errors are introduced. A comparison with general-purpose industrial whitening sprays is also presented, and achieved results show no significant differences in the quality of the resulting 3D models.

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This paper addresses the problem of automatic reconstruction of ancient artifacts from archaeological fragments. The technique described here focuses on pairwise matching of flat fragments (typically fresco fragments), and it is intended to be the core of a larger system for artifact reconstruction. Global registration techniques are challenging due to the combinatory explosion that happens in the solution space: the goal is to find the best alignment among all possible ones without an initialization. This fact defines the duality between performance and correction that we face in this work. The proposed technique defines a cost function to evaluate the quality of an alignment based on a discrete sampling of the fragments that ensures data alignment. Starting from an exhaustive search strategy, the technique progressively incorporates new features that lead to a hierarchical search strategy. Convergence and correction of the resulting technique are ensured using an optimistic cost function. Internal search calculations are optimized so the only operations performed are additions, subtractions and comparisons over aligned data. All heavy geometric operations are carried out by the GPU on a pre-processing stage that only happens once per fragment.

This paper deals with storage and transportation of fragile heritage objets. The main goal is to create an ad-hoc packaging software solution by combining the use of 3D scanning and 3D milling technologies. Traditionally, packaging for this kind of objects is made manually, creating custom supports and boxes adapted to the specific needs and particularities of each case. This process is time-consuming and fully dependent on the conservator's skills. On the other hand, the proposed solution provides a faster execution and a standard solution adapted to every object. The process starts with the acquisition of 3D digital models of the original artifacts and uses an interactive GPU-accelerated algorithm to assist the conservator in the construction of the package. The final result is a CNC program that is sent to a low cost milling machine to produce the housing for the selected artifact. Unlike 3D printers, milling machines allow working with lots of different materials, which is a key aspect for this kind of packages: chemical stability and shock absorption are mandatory. Given that, during the design process, original artifacts are only manipulated in the acquisition stage, risks of damaging them are also reduced. Since the whole process is considered in the same application, intermediate calculations are performed considering manufacturing and user-defined restrictions. This way, together with a GPU implementation, achieved results are very fast providing a real-time application with visual feed-back.

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This paper addresses the challenges of rendering massive indoor point clouds in Virtual Reality. In these kind of visualizations the point of view is never static, imposing the need of a one-shot (i.e. non-iterative) rendering strategy, in contrast with progressive refinement approaches that assume that the camera position does not change between most consecutive frames. Our approach benefits from the static nature of indoor environments to pre-compute a visibility map that enables us to boost real-time rendering performance. The key idea behind our visibility map is to exploit the cluttered topology of buildings in order to effectively cull the regions of the space that are occluded by structural elements such as walls. This does not only improve performance but also the visual quality of the final render, allowing us to display in full detail the space and preventing the user to see the contiguous spaces through the walls. Additionally, we introduce a novel hierarchical data structure that enables us to display the point cloud with a continuous level of detail with a minimal impact on performance. Experimental results show that our approach outperforms state-of-the-art techniques in complex indoor environments and achieves comparable results in outdoor ones, proving the generality of our method.