Browsing by Author "Xin, Shiqing"

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    Coverage Axis: Inner Point Selection for 3D Shape Skeletonization
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Dou, Zhiyang; Lin, Cheng; Xu, Rui; Yang, Lei; Xin, Shiqing; Komura, Taku; Wang, Wenping; Chaine, Raphaƫlle; Kim, Min H.
    In this paper, we present a simple yet effective formulation called Coverage Axis for 3D shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the surface points using as few inside medial balls as possible. This formulation inherently induces a compact and expressive approximation of the Medial Axis Transform (MAT) of a given shape. Different from previous methods that rely on local approximation error, our method allows a global consideration of the overall shape structure, leading to an efficient high-level abstraction and superior robustness to noise. Another appealing aspect of our method is its capability to handle more generalized input such as point clouds and poor-quality meshes. Extensive comparisons and evaluations demonstrate the remarkable effectiveness of our method for generating compact and expressive skeletal representation to approximate the MAT.
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    Robust Computation of 3D Apollonius Diagrams
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Peihui; Yuan, Na; Ma, Yuewen; Xin, Shiqing; He, Ying; Chen, Shuangmin; Xu, Jian; Wang, Wenping; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Apollonius diagrams, also known as additively weighted Voronoi diagrams, are an extension of Voronoi diagrams, where the weighted distance is defined by the Euclidean distance minus the weight. The bisectors of Apollonius diagrams have a hyperbolic form, which is fundamentally different from traditional Voronoi diagrams and power diagrams. Though robust solvers are available for computing 2D Apollonius diagrams, there is no practical approach for the 3D counterpart. In this paper, we systematically analyze the structural features of 3D Apollonius diagrams, and then develop a fast algorithm for robustly computing Apollonius diagrams in 3D. Our algorithm consists of vertex location, edge tracing and face extraction, among which the key step is to adaptively subdivide the initial large box into a set of sufficiently small boxes such that each box contains at most one Apollonius vertex. Finally, we use centroidal Voronoi tessellation (CVT) to discretize the curved bisectors with well-tessellated triangle meshes. We validate the effectiveness and robustness of our algorithm through extensive evaluation and experiments. We also demonstrate an application on computing centroidal Apollonius diagram.
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    SRF-Net: Spatial Relationship Feature Network for Tooth Point Cloud Classification
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ma, Qian; Wei, Guangshun; Zhou, Yuanfeng; Pan, Xiao; Xin, Shiqing; Wang, Wenping; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    3D scanned point cloud data of teeth is popular used in digital orthodontics. The classification and semantic labelling for point cloud of each tooth is a key and challenging task for planning dental treatment. Utilizing the priori ordered position information of tooth arrangement, we propose an effective network for tooth model classification in this paper. The relative position and the adjacency similarity feature vectors are calculated for tooth 3D model, and combine the geometric feature into the fully connected layers of the classification training task. For the classification of dental anomalies, we present a dental anomalies processing method to improve the classification accuracy. We also use FocalLoss as the loss function to solve the sample imbalance of wisdom teeth. The extensive evaluations, ablation studies and comparisons demonstrate that the proposed network can classify tooth models accurately and automatically and outperforms state-of-the-art point cloud classification methods.