| 面向可展特征的网格模型去噪方法 |
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| 作者姓名： | 桂杰 曹力 伯彭波 顾兆光 |
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| 作者单位： | 1. 合肥工业大学计算机与信息学院，安徽 合肥 230601；
2. 安全关键工业测控技术教育部工程研究中心，安徽 合肥 230601；
3. 哈尔滨工业大学(威海)计算机科学与技术学院，山东 威海 264209；
4. 香港量子人工智能实验室有限公司，香港 999077 |
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| 基金项目： | 国家重点研发计划重点专项(2020YFC1523100)；安徽省重点研究与开发计划项目(202104e11020006)；国家自然科学基金项目(62072139，61602146) |
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| 摘 要： | 可展特征是三维网格模型的常见几何特征。为了更好地对具备可展特征的网格模型进行去噪，提出一种面向可展特征的网格模型去噪方法。首先基于变分形状逼近策略分割可展区域，识别出网格模型上可展特征区域，并对分割区域进行基于可展性度量的合并和划分，改进现有L0去噪算法中针对非均匀噪声网格的正则优化表达项，引入三角网格顶点的可展度量项，利用可展特征的曲面法向量L0范数的优化问题求解实现网格模型的去噪。通过对多个模型数据集中的大量模型数据进行处理，验证了该方法的有效性。实验表明，结合模型的可展特性的去噪方法在保持模型的几何特征特别是可展特征上效果优于已有方法。
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| 关 键 词： | 计算机图形学 网格模型处理 网格模型去噪 可展性分析 L0范数 |
Denoise method for meshes with developable features |
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| Authors: | GUI Jie CAO Li BO Peng-bo KOO Siu-kong |
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| Affiliation: | 1. School of Computer and Information, Hefei University of Technology, Hefei Anhui, 230601, China;
2. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei Anhui 230601, China;
3. School of Computer Science and Technology, Harbin Institute of Technology (Weihai), Weihai Shandong 264209, China;
4. Hong Kong Quantum AI Lab, Hong Kong 999077, China |
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| Abstract: | Developable features can be commonly found in various meshes from different datasets. A novel denoising method was proposed for meshes with developable features. The developable features can be reflected by the sum of vertex interior angles of the mesh and well processed by L 0 computation. According to the definition of the developability of the mesh, the existing L 0 denoising algorithm was improved, and the sum of the internal angles formed in the neighborhood of a certain point was constrained to obtain the denoising effect conforming to the developable features. Compared with the existing methods, the denoising method combined with the developable features of the model can denoise more effectively while maintaining the original shape and the developable features of the model. The proposed method is particularly superior in the case of processing a large number of model data in multiple meshes datasets. |
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| Keywords: | computer graphics model processing mesh denoising develop analysis developable features L 0 norm |
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