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Now showing 1 - 7 of 7
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    Smooth Blended Subdivision Shading
    (The Eurographics Association, 2018) Bakker, Jelle; Barendrecht, Pieter J.; Kosinka, Jiri; Diamanti, Olga and Vaxman, Amir
    The concept known as subdivision shading aims at improving the shading of subdivision surfaces. It is based on the subdivision of normal vectors associated with the control net of the surface. By either using the resulting subdivided normal field directly, or blending it with the normal field of the limit surface, renderings of higher visual smoothness can be obtained. In this work we propose a different and more versatile approach to blend the two normal fields, yielding not only better results, but also a proof that our blended normal field is C1.
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    Shape-from-Operator: Recovering Shapes from Intrinsic Operators
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Boscaini, Davide; Eynard, Davide; Kourounis, Drosos; Bronstein, Michael M.; Olga Sorkine-Hornung and Michael Wimmer
    We formulate the problem of shape-from-operator (SfO), recovering an embedding of a mesh from intrinsic operators defined through the discrete metric (edge lengths). Particularly interesting instances of our SfO problem include: shape-from-Laplacian, allowing to transfer style between shapes; shape-from-difference operator, used to synthesize shape analogies; and shape-from-eigenvectors, allowing to generate 'intrinsic averages' of shape collections. Numerically, we approach the SfO problem by splitting it into two optimization sub-problems: metric-from-operator (reconstruction of the discrete metric from the intrinsic operator) and embedding-from-metric (finding a shape embedding that would realize a given metric, a setting of the multidimensional scaling problem). We study numerical properties of our problem, exemplify it on several applications, and discuss its imitations.
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    Growing Circles: A Region Growing Algorithm for Unstructured Grids and Non-aligned Boundaries
    (The Eurographics Association, 2018) Dabbaghchian, Saeed; Jain, Eakta and Kosinka, Jirí
    Detecting the boundaries of an enclosed region is a problem which arises in some applications such as the human upper airway modeling. Using of standard algorithms fails because of the inevitable errors, i.e. gaps and overlaps between the surrounding boundaries. Growing circles is an automatic approach to address this problem. A circle is centered inside the region and starts to grow by increasing its radius. Its growth is limited either by the surrounding boundaries or by reaching its maximum radius. To deal with complex shapes, many circles are used in which each circle partially reconstructs the region, and the whole region is determined by the union of these partial regions. The center of the circles and their maximum radius are calculated adaptively. It is similar to the region growing algorithm which is widely used in image processing applications. However, it works for unstructured grids as well as Cartesian ones. As an application of the method, it is applied to detect the boundaries of the upper airway cross-sections.
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    Iterative Carving for Self-supporting 3D Printed Cavities
    (The Eurographics Association, 2018) Hornus, Samuel; Lefebvre, Sylvain; Diamanti, Olga and Vaxman, Amir
    Additive manufacturing technologies fabricate objects layer by layer, adding material on top of already solidified layers. A key challenge is to ensure that there is always material below, for otherwise added material simply falls under the effect of gravity. This is a critical issue with most technologies, and with fused filament in particular. In this work we investigate how to compute as large as possible empty cavities which boundaries are self-supporting. Our technique is based on an iterated carving algorithm, that is fast to compute and produces nested sets of inner walls. The walls have exactly the minimal printable thickness of the manufacturing process everywhere. Remarkably, our technique is out-of-core, sweeping through the model from the top down. Using our approach, we can print large objects using as little as a single filament thickness for the boundary, providing one order of magnitude reduction in print time and material use while guaranteeing printability.
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    A Survey on Data-driven Dictionary-based Methods for 3D Modeling
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Lescoat, Thibault; Ovsjanikov, Maks; Memari, Pooran; Thiery, Jean-Marc; Boubekeur, Tamy; Hildebrandt, Klaus and Theobalt, Christian
    Dictionaries are very useful objects for data analysis, as they enable a compact representation of large sets of objects through the combination of atoms. Dictionary-based techniques have also particularly benefited from the recent advances in machine learning, which has allowed for data-driven algorithms to take advantage of the redundancy in the input dataset and discover relations between objects without human supervision or hard-coded rules. Despite the success of dictionary-based techniques on a wide range of tasks in geometric modeling and geometry processing, the literature is missing a principled state-of-the-art of the current knowledge in this field. To fill this gap, we provide in this survey an overview of data-driven dictionary-based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary-based methods suitable for 3D data synthesis, with applications in geometric modeling and design. Our ultimate goal is to enlight the fact that these techniques can be used to combine the data-driven paradigm with design intent to synthesize new plausible objects with minimal human intervention. This is the main motivation to restrict the scope of the present survey to techniques handling point clouds and meshes, making use of dictionaries whose definition depends on the input data, and enabling shape reconstruction or synthesis through the combination of atoms.
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    State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Zollhöfer, Michael; Thies, Justus; Garrido, Pablo; Bradley, Derek; Beeler, Thabo; Pérez, Patrick; Stamminger, Marc; Nießner, Matthias; Theobalt, Christian; Hildebrandt, Klaus and Theobalt, Christian
    The computer graphics and vision communities have dedicated long standing efforts in building computerized tools for reconstructing, tracking, and analyzing human faces based on visual input. Over the past years rapid progress has been made, which led to novel and powerful algorithms that obtain impressive results even in the very challenging case of reconstruction from a single RGB or RGB-D camera. The range of applications is vast and steadily growing as these technologies are further improving in speed, accuracy, and ease of use. Motivated by this rapid progress, this state-of-the-art report summarizes recent trends in monocular facial performance capture and discusses its applications, which range from performance-based animation to real-time facial reenactment. We focus our discussion on methods where the central task is to recover and track a three dimensional model of the human face using optimization-based reconstruction algorithms. We provide an in-depth overview of the underlying concepts of real-world image formation, and we discuss common assumptions and simplifications that make these algorithms practical. In addition, we extensively cover the priors that are used to better constrain the under-constrained monocular reconstruction problem, and discuss the optimization techniques that are employed to recover dense, photo-geometric 3D face models from monocular 2D data. Finally, we discuss a variety of use cases for the reviewed algorithms in the context of motion capture, facial animation, as well as image and video editing.
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    Anisotropic Diffusion Descriptors
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Boscaini, Davide; Masci, Jonathan; Rodolà, Emanuele; Bronstein, Michael M.; Cremers, Daniel; Joaquim Jorge and Ming Lin
    Spectral methods have recently gained popularity in many domains of computer graphics and geometry processing, especially shape processing, computation of shape descriptors, distances, and correspondence. Spectral geometric structures are intrinsic and thus invariant to isometric deformations, are efficiently computed, and can be constructed on shapes in different representations. A notable drawback of these constructions, however, is that they are isotropic, i.e., insensitive to direction. In this paper, we show how to construct direction-sensitive spectral feature descriptors using anisotropic diffusion on meshes and point clouds. The core of our construction are directed local kernels acting similarly to steerable filters, which are learned in a task-specific manner. Remarkably, while being intrinsic, our descriptors allow to disambiguate reflection symmetries. We show the application of anisotropic descriptors for problems of shape correspondence on meshes and point clouds, achieving results significantly better than state-of-the-art methods.