Browsing by Author "Wonka, Peter"

Now showing 1 - 4 of 4
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    Customized Summarizations of Visual Data Collections
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Yuan, Mengke; Ghanem, Bernard; Yan, Dong‐Ming; Wu, Baoyuan; Zhang, Xiaopeng; Wonka, Peter; Benes, Bedrich and Hauser, Helwig
    We propose a framework to generate customized summarizations of visual data collections, such as collections of images, materials, 3D shapes, and 3D scenes. We assume that the elements in the visual data collections can be mapped to a set of vectors in a feature space, in which a fitness score for each element can be defined, and we pose the problem of customized summarizations as selecting a subset of these elements. We first describe the design choices a user should be able to specify for modeling customized summarizations and propose a corresponding user interface. We then formulate the problem as a constrained optimization problem with binary variables and propose a practical and fast algorithm based on the alternating direction method of multipliers (ADMM). Our results show that our problem formulation enables a wide variety of customized summarizations, and that our solver is both significantly faster than state‐of‐the‐art commercial integer programming solvers and produces better solutions than fast relaxation‐based solvers.
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    Discrete Optimization for Shape Matching
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Ren, Jing; Melzi, Simone; Wonka, Peter; Ovsjanikov, Maks; Digne, Julie and Crane, Keenan
    We propose a novel discrete solver for optimizing functional map-based energies, including descriptor preservation and promoting structural properties such as area-preservation, bijectivity and Laplacian commutativity among others. Unlike the commonly-used continuous optimization methods, our approach enforces the functional map to be associated with a pointwise correspondence as a hard constraint, which provides a stronger link between optimized properties of functional and point-topoint maps. Under this hard constraint, our solver obtains functional maps with lower energy values compared to the standard continuous strategies. Perhaps more importantly, the recovered pointwise maps from our discrete solver preserve the optimized for functional properties and are thus of higher overall quality. We demonstrate the advantages of our discrete solver on a range of energies and shape categories, compared to existing techniques for promoting pointwise maps within the functional map framework. Finally, with this solver in hand, we introduce a novel Effective Functional Map Refinement (EFMR) method which achieves the state-of-the-art accuracy on the SHREC'19 benchmark.
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    Local Editing of Procedural Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lipp, Markus; Specht, Matthias; Lau, Cheryl; Wonka, Peter; Mueller, Pascal; Alliez, Pierre and Pellacini, Fabio
    Procedural modeling is used across many industries for rapid 3D content creation. However, professional procedural tools often lack artistic control, requiring manual edits on baked results, diminishing the advantages of a procedural modeling pipeline. Previous approaches to enable local artistic control require special annotations of the procedural system and manual exploration of potential edit locations. Therefore, we propose a novel approach to discover meaningful and non-redundant good edit locations (GELs). We introduce a bottom-up algorithm for finding GELs directly from the attributes in procedural models, without special annotations. To make attribute edits at GELs persistent, we analyze their local spatial context and construct a meta-locator to uniquely specify their structure. Meta-locators are calculated independently per attribute, making them robust against changes in the procedural system. Functions on meta-locators enable intuitive and robust multi-selections. Finally, we introduce an algorithm to transfer meta-locators to a different procedural model. We show that our approach greatly simplifies the exploration of the local edit space, and we demonstrate its usefulness in a user study and multiple real-world examples.
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    Structured Regularization of Functional Map Computations
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ren, Jing; Panine, Mikhail; Wonka, Peter; Ovsjanikov, Maks; Bommes, David and Huang, Hui
    We consider the problem of non-rigid shape matching using the functional map framework. Specifically, we analyze a commonly used approach for regularizing functional maps, which consists in penalizing the failure of the unknown map to commute with the Laplace-Beltrami operators on the source and target shapes. We show that this approach has certain undesirable fundamental theoretical limitations, and can be undefined even for trivial maps in the smooth setting. Instead we propose a novel, theoretically well-justified approach for regularizing functional maps, by using the notion of the resolvent of the Laplacian operator. In addition, we provide a natural one-parameter family of regularizers, that can be easily tuned depending on the expected approximate isometry of the input shape pair. We show on a wide range of shape correspondence scenarios that our novel regularization leads to an improvement in the quality of the estimated functional, and ultimately pointwise correspondences before and after commonly-used refinement techniques.