36-Issue 7

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https://diglib.eg.org/handle/10.2312/2631805

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Browsing 36-Issue 7 by Subject "Geometric algorithms"

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    Data-Driven Sparse Priors of 3D Shapes
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Remil, Oussama; Xie, Qian; Xie, Xingyu; Xu, Kai; Wang, Jun; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    We present a sparse optimization framework for extracting sparse shape priors from a collection of 3D models. Shape priors are defined as point-set neighborhoods sampled from shape surfaces which convey important information encompassing normals and local shape characterization. A 3D shape model can be considered to be formed with a set of 3D local shape priors, while most of them are likely to have similar geometry. Our key observation is that the local priors extracted from a family of 3D shapes lie in a very low-dimensional manifold. Consequently, a compact and informative subset of priors can be learned to efficiently encode all shapes of the same family. A comprehensive library of local shape priors is first built with the given collection of 3D models of the same family. We then formulate a global, sparse optimization problem which enforces selecting representative priors while minimizing the reconstruction error. To solve the optimization problem, we design an efficient solver based on the Augmented Lagrangian Multipliers method (ALM). Extensive experiments exhibit the power of our data-driven sparse priors in elegantly solving several high-level shape analysis applications and geometry processing tasks, such as shape retrieval, style analysis and symmetry detection.
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    Regression-Based Landmark Detection on Dynamic Human Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Jang, Deok-Kyeong; Lee, Sung-Hee; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    Detecting anatomical landmarks on various human models with dynamic poses remains an important and challenging problem in computer graphics research. We present a novel framework that consists of two-level regressors for finding correlations between human shapes and landmark positions in both body part and holistic scales. To this end, we first develop pose invariant coordinates of landmarks that represent both local and global shape features by using the pose invariant local shape descriptors and their spatial relationships. Our body part-level regression deals with the shape features from only those body parts that correspond to a certain landmark. In order to do this, we develop a method that identifies such body parts per landmark, by using geometric shape dictionary obtained through the bag of features method. Our method is nearly automatic, as it requires human assistance only once to differentiate the left and right sides. The method also shows the prediction accuracy comparable to or better than those of existing methods, with a test data set containing a large variation of human shapes and poses.