Browsing by Author "Tao, Wenyuan"

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    Exploring Multi-dimensional Data via Subset Embedding
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Xie, Peng; Tao, Wenyuan; Li, Jie; Huang, Wentao; Chen, Siming; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Multi-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformlyformatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully-connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3-step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.
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    The Layerizing VoxPoint Annular Convolutional Network for 3D Shape Classification
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Tong; Tao, Wenyuan; Own, Chung-Ming; Lou, Xiantuo; Zhao, Yuehua; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Analyzing the geometric and semantic properties of 3D point cloud data via the deep learning networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. In our study, the authors combine the advantage of voxels and point clouds by presenting a new data form of voxel models, called Layer-Ring data. This data type can retain the fine description of the 3D data, and keep the high efficiency of feature extraction. After that, based on the Layer-Ring data, a modern network architecture, called VoxPoint Annular Network (VAN), works on the Layer-Ring data for the feature extraction and object category prediction. The design idea is based on the edge-extraction and the coordinate representation for each voxel on the separated layer. With the flexible design, our proposed VAN can adapt to the layer's geometric variability and scalability. Finally, the extensive experiments and comparisons demonstrate that our approach obtained the notable results with the state-of-the-art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40). Moreover, the tests also proved that 3D shape features could learn efficiently and robustly. All relevant codes will be available at https://github.com/helloFionaQ/Vox-PointNet.