Browsing by Author "Yan, Dong-Ming"

Now showing 1 - 5 of 5
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    Anisotropic Surface Remeshing without Obtuse Angles
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Xu, Qun-Ce; Yan, Dong-Ming; Li, Wenbin; Yang, Yong-Liang; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    We present a novel anisotropic surface remeshing method that can efficiently eliminate obtuse angles. Unlike previous work that can only suppress obtuse angles with expensive resampling and Lloyd-type iterations, our method relies on a simple yet efficient connectivity and geometry refinement, which can not only remove all the obtuse angles, but also preserves the original mesh connectivity as much as possible. Our method can be directly used as a post-processing step for anisotropic meshes generated from existing algorithms to improve mesh quality. We evaluate our method by testing on a variety of meshes with different geometry and topology, and comparing with representative prior work. The results demonstrate the effectiveness and efficiency of our approach.
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    Feature Curve Network Extraction via Quadric Surface Fitting
    (The Eurographics Association, 2019) Zhengda, Lu; Guo, Jianwei; Xiao, Jun; Wang, Ying; Zhang, Xiaopeng; Yan, Dong-Ming; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Feature curves on 3D shapes provide a high dimensional representation of the geometry and reveal their underlying structure. In this paper, we present an automatic approach for extracting complete feature curve networks from 3D models, as well as generating a high-quality patch layout. Starting from an initial collection of noisy and fragmented feature curves, we first filter non-salient or noisy feature curves by utilizing a quadric surface fitting technique. We then handle the curve intersections and curve missing by conducting a feature extension step to form a closed feature curve network. Finally, we generate a patch layout to reveal a highly structured representation of the input surfaces. Experimental results demonstrate that our algorithm is robust for extracting complete feature curve networks from complex input meshes and achieves superior quality patch layouts compared with the state-of-the-art approaches.
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    Pixel-wise Dense Detector for Image Inpainting
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhang, Ruisong; Quan, Weize; Wu, Baoyuan; Li, Zhifeng; Yan, Dong-Ming; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts. Moreover, the adversarial loss and reconstruction loss (e.g., `1 loss) are combined with tradeoff weights, which are also difficult to tune. In this paper, we propose a novel detection-based generative framework for image inpainting, which adopts the min-max strategy in an adversarial process. The generator follows an encoder-decoder architecture to fill the missing regions, and the detector using weakly supervised learning localizes the position of artifacts in a pixel-wise manner. Such position information makes the generator pay attention to artifacts and further enhance them. More importantly, we explicitly insert the output of the detector into the reconstruction loss with a weighting criterion, which balances the weight of the adversarial loss and reconstruction loss automatically rather than manual operation. Experiments on multiple public datasets show the superior performance of the proposed framework. The source code is available at https://github.com/Evergrow/GDN_Inpainting.
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    PowerRTF: Power Diagram based Restricted Tangent Face for Surface Remeshing
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Yao, Yuyou; Liu, Jingjing; Fei, Yue; Wu, Wenming; Zhang, Gaofeng; Yan, Dong-Ming; Zheng, Liping; Memari, Pooran; Solomon, Justin
    Triangular meshes of superior quality are important for geometric processing in practical applications. Existing approximative CVT-based remeshing methodology uses planar polygonal facets to fit the original surface, simplifying the computational complexity. However, they usually do not consider surface curvature. Topological errors and outliers can also occur in the close sheet surface remeshing, resulting in wrong meshes. With this regard, we present a novel method named PowerRTF, an extension of the restricted tangent face (RTF) in conjunction with the power diagram, to better approximate the original surface with curvature adaption. The idea is to introduce a weight property to each sample point and compute the power diagram on the tangent face to produce area-controlled polygonal facets. Based on this, we impose the variable-capacity constraint and centroid constraint to the PowerRTF, providing the trade-off between mesh quality and computational efficiency. Moreover, we apply a normal verification-based inverse side point culling method to address the topological errors and outliers in close sheet surface remeshing. Our method independently computes and optimizes the PowerRTF per sample point, which is efficiently implemented in parallel on the GPU. Experimental results demonstrate the effectiveness, flexibility, and efficiency of our method.
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    WTFM Layer: An Effective Map Extractor for Unsupervised Shape Correspondence
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Liu, Shengjun; Xu, Haojun; Yan, Dong-Ming; Hu, Ling; Liu, Xinru; Li, Qinsong; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    We propose a novel unsupervised learning approach for computing correspondences between non-rigid 3D shapes. The core idea is that we integrate a novel structural constraint into the deep functional map pipeline, a recently dominant learning framework for shape correspondence, via a powerful spectral manifold wavelet transform (SMWT). As SMWT is isometrically invariant operator and can analyze features from multiple frequency bands, we use the multiscale SMWT results of the learned features as function preservation constraints to optimize the functional map by assuming each frequency-band information of the descriptors should be correspondingly preserved by the functional map. Such a strategy allows extracting significantly more deep feature information than existing approaches which only use the learned descriptors to estimate the functional map. And our formula strongly ensure the isometric properties of the underlying map. We also prove that our computation of the functional map amounts to filtering processes only referring to matrix multiplication. Then, we leverage the alignment errors of intrinsic embedding between shapes as a loss function and solve it in an unsupervised way using the Sinkhorn algorithm. Finally, we utilize DiffusionNet as a feature extractor to ensure that discretization-resistant and directional shape features are produced. Experiments on multiple challenging datasets prove that our method can achieve state-of-the-art correspondence quality. Furthermore, our method yields significant improvements in robustness to shape discretization and generalization across the different datasets. The source code and trained models will be available at https://github.com/HJ-Xu/ WTFM-Layer.