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Item Computational Design of Fabricable Geometric Patterns(The Eurographics Association, 2023) Scandurra, Elena; Laccone, Francesco; Malomo, Luigi; Callieri, Marco; Cignoni, Paolo; Giorgi, Daniela; Banterle, Francesco; Caggianese, Giuseppe; Capece, Nicola; Erra, Ugo; Lupinetti, Katia; Manfredi, GildaThis paper addresses the design of surfaces as assemblies of geometric patterns with predictable performance in response to mechanical stimuli. We design a family of tileable and fabricable patterns represented as triangle meshes, which can be assembled for creating surface tessellations. First, a regular recursive subdivision of the planar space generates different geometric configurations for candidate patterns, having interesting and varied aesthetic properties. Then, a refinement step addresses manufacturability by solving for non-manifold configurations and sharp angles which would produce disconnected or fragile patterns. We simulate our patterns to evaluate their mechanical response when loaded in different scenarios targeting out-of-plane bending. Through a simple browsing interface, we show that our patterns span a variety of different bending behaviors. The result is a library of patterns with varied aesthetics and predefined mechanical behavior, to use for the direct design of mechanical metamaterials. To assess the feasibility of our approach, we show a pair of fabricated 3D objects with different curvatures.Item GPU-Accelerating Hierarchical Descriptors for Point Set Registration(The Eurographics Association, 2023) Dutta, Somnath; Russig, Benjamin; Gumhold, Stefan; Banterle, Francesco; Caggianese, Giuseppe; Capece, Nicola; Erra, Ugo; Lupinetti, Katia; Manfredi, GildaWe present a GPU-accelerated global registration method for registering partial shapes, a common and often performancecritical task in many robotics, vision, and graphics applications. Global registration based on descriptor matching is highly dependent on the quality at which a shape is sampled, and computing expressive descriptors typically incurs high computation time. In this paper, we augment a global pair-wise registration algorithm based on hierarchical shape descriptors with a GPU-accelerated descriptor construction process, reducing the time spent on building descriptors by an order of magnitude. This allows for building more expressive descriptors, achieving a dual gain in both performance and accuracy. We conducted extensive evaluations on a large set of pair-wise registration problems, demonstrating very competitive registration accuracy, often rendering subsequent refinement with a local method unnecessary.