Browsing by Author "Zhang, Yu‐Wei"

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    Computer-assisted Relief Modelling: A Comprehensive Survey
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhang, Yu-Wei; Wu, Jing; Ji, Zhongping; Wei, Mingqiang; Zhang, Caiming; Giachetti, Andrea and Rushmeyer, Holly
    As an art form between drawing and sculpture, relief has been widely used in a variety of media for signs, narratives, decorations and other purposes. Traditional relief creation relies on both professional skills and artistic expertise, which is extremely timeconsuming. Recently, automatic or semi-automatic relief modelling from a 3D object or a 2D image has been a subject of interest in computer graphics. Various methods have been proposed to generate reliefs with few user interactions or minor human efforts, while preserving or enhancing the appearance of the input. This survey provides a comprehensive review of the advances in computer-assisted relief modelling during the past decade. First, we provide an overview of relief types and their art characteristics. Then, we introduce the key techniques of object-space methods and image-space methods respectively. Advantages and limitations of each category are discussed in details. We conclude the report by discussing directions for possible future research.
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    A Deep Residual Network for Geometric Decontouring
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ji, Zhongping; Zhou, Chengqin; Zhang, Qiankan; Zhang, Yu-Wei; Wang, Wenping; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Grayscale images are intensively used to construct or represent geometric details in field of computer graphics. In practice, displacement mapping technique often allows an 8-bit grayscale image input to manipulate the position of vertices. Human eyes are insensitive to the change of intensity between consecutive gray levels, so a grayscale image only provides 256 levels of luminances. However, when the luminances are converted into geometric elements, certain artifacts such as false contours become obvious. In this paper, we formulate the geometric decontouring as a constrained optimization problem from a geometric perspective. Instead of directly solving this optimization problem, we propose a data-driven method to learn a residual mapping function. We design a Geometric DeContouring Network (GDCNet) to eliminate the false contours effectively. To this end, we adopt a ResNet-based network structure and a normal-based loss function. Extensive experimental results demonstrate that accurate reconstructions can be achieved effectively. Our method can be used as a relief compressed representation and enhance the traditional displacement mapping technique to augment 3D models with high-quality geometric details using grayscale images efficiently.