EG2016

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https://diglib.eg.org/handle/10.2312/15062

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Browsing EG2016 by Subject "and systems"

Now showing 1 - 4 of 4
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    Building Construction Sets by Tiling Grammar Simplification
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Kalojanov, Javor; Wand, Michael; Slusallek, Philipp; Joaquim Jorge and Ming Lin
    This paper poses the problem of fabricating physical construction sets from example geometry: A construction set provides a small number of different types of building blocks from which the example model as well as many similar variants can be reassembled. This process is formalized by tiling grammars. Our core contribution is an approach for simplifying tiling grammars such that we obtain physically manufacturable building blocks of controllable granularity while retaining variability, i.e., the ability to construct many different, related shapes. Simplification is performed by sequences of two types of elementary operations: non-local joint edge collapses in the tile graphs reduce the granularity of the decomposition and approximate replacement operations reduce redundancy. We evaluate our method on abstract graph grammars in addition to computing several physical construction sets, which are manufactured using a commodity 3D printer.
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    Learning 3D Deformation of Animals from 2D Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Kanazawa, Angjoo; Kovalsky, Shahar; Basri, Ronen; Jacobs, David; Joaquim Jorge and Ming Lin
    Understanding how an animal can deform and articulate is essential for a realistic modification of its 3D model. In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal. We present a volumetric deformation framework that produces a set of new 3D models by deforming a template 3D model according to a set of user-clicked images. Our framework is based on a novel locally-bounded deformation energy, where every local region has its own stiffness value that bounds how much distortion is allowed at that location. We jointly learn the local stiffness bounds as we deform the template 3D mesh to match each user-clicked image. We show that this seemingly complex task can be solved as a sequence of convex optimization problems. We demonstrate the effectiveness of our approach on cats and horses, which are highly deformable and articulated animals. Our framework produces new 3D models of animals that are significantly more plausible than methods without learned stiffness.
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    Mesh Saliency Analysis via Local Curvature Entropy
    (The Eurographics Association, 2016) Limper, Max; Kuijper, Arjan; Fellner, Dieter W.; T. Bashford-Rogers and L. P. Santos
    We present a novel approach for estimating mesh saliency. Our method is fast, flexible, and easy to implement. By applying the well-known concept of Shannon entropy to 3D mesh data, we obtain an efficient method to determine mesh saliency. Comparing our method to the most recent, state-of-the-art approach, we show that results of at least similar quality can be achieved within a fraction of the original computation time. We present saliency-guided mesh simplification as a possible application.
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    Smooth Interpolation of Curve Networks with Surface Normals
    (The Eurographics Association, 2016) Stanko, Tibor; Hahmann, Stefanie; Bonneau, Georges-Pierre; Saguin-Sprynski, Nathalie; T. Bashford-Rogers and L. P. Santos
    Recent surface acquisition technologies based on microsensors produce three-space tangential curve data which can be transformed into a network of space curves with surface normals. This paper addresses the problem of surfacing an arbitrary closed 3D curve network with given surface normals. Thanks to the normal vector input, the patch finding problem can be solved unambiguously and an initial piecewise smooth triangle mesh is computed. The input normals are propagated throughout the mesh and used to compute mean curvature vectors. We then introduce a new variational optimization method in which the standard bi-Laplacian is penalized by a term based on the mean curvature vectors. The intuition behind this original approach is to guide the standard Laplacian-based variational methods by the curvature information extracted from the input normals. The normal input increases shape fidelity and allows to achieve globally smooth and visually pleasing shapes.