| 逼近点云的三角形网格的边缘误差减小研究 |
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| 引用本文： | 张伟,曾立,檀中强.逼近点云的三角形网格的边缘误差减小研究[J].计算机工程与应用,2011,35(35):196-198. |
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| 作者姓名： | 张伟 曾立 檀中强 |
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| 作者单位： | 1. 中国计量学院机电工程学院,杭州,310018
2. 浙江大学城市学院工程学院,杭州,310015 |
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| 基金项目： | 浙江省自然科学基金(No.Y1091012); 国家质检总局科技计划项目(No.2006QK65) |
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| 摘 要： | 基于SOFM神经网络构建的三角形网格模型可以实现测量点云压缩后的Delaunay三角逼近剖分,但该模型存在边缘误差。为减小三角形网格的边缘误差,改进了三角形网格模型的训练模式,提出了3步训练模式。第1步采用整个测量点云,对三角形网格模型中的所有神经元进行整体训练;第2步采用测量点云中的边界点集,对三角形网格模型中的网格边界神经元进行训练;第3步采用边界点集中的角点点集,对与边界角点匹配最佳的网格边界神经元进行训练。算例表明,应用该训练模式,可以有效减小三角形网格的边缘误差,三角形网格逼近散乱点云的逼近精度得到提高并覆盖散乱点云整体分布范围。
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| 关 键 词： | 逆向工程 三角形网格 神经网络 边缘误差 散乱点云 |
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Research on boundary error reduction of triangle mesh reconstruction for unorganized point cloud |
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| ZHANG Wei
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ZENG Li
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TAN Zhongqiang.Research on boundary error reduction of triangle mesh reconstruction for unorganized point cloud[J].Computer Engineering and Applications,2011,35(35):196-198. |
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| Authors: | ZHANG Wei ZENG Li TAN Zhongqiang |
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| Affiliation: | ZHANG Wei1,ZENG Li2,TAN Zhongqiang1 1.College of Mechanical and Electronic Engineering,China Jiliang University,Hangzhou 310018,China 2.Engineering Department,Zhejiang University City College,Hangzhou 310015,China |
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| Abstract: | An approach based on the Self-Organizing Feature Map(SOFM) neural network has been developed to reconstruct triangle mesh for the unorganized digitized point cloud.However the approach suffers from boundary problems.A three-steps training method is proposed in order to reduce the boundary error.All the neurons of the mesh model are trained directly over the unroganized digitized point-cloud.Only the boundary neurons of the mesh model is undergo trained by the boundary points of the digitized point-cloud.Onl... |
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| Keywords: | reverse engineering triangle mesh neural network boundary error unorganized point cloud
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