Surface Reconstruction from Unorganized Points Using Self-Organizing Neural Networks
Surface Reconstruction from Unorganized Points Using Self-Organizing Neural Networks
复制标题
使用自组织神经网络从无组织点重建表面
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
Yizhou Yu
中科院分区：
文献类型：
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作者：
Yizhou Yu
介绍了一种基于Kohonen自组织映射的散乱点曲面重建新技术。曲面的拓扑是预先确定的，并采用神经网络学习算法来获得曲面每个顶点上的正确3D坐标。提出了边缘互换和多分辨率学习的方法，使算法更有效、更高效。整个算法实现起来非常简单。实验结果表明，该方法是成功的。
We introduce a novel technique for surface reconstruction from unorganized points by applying Kohonen’s self-organizing map. The topology of the surface is predetermined, and a neural network learning algorithm is carried out to obtain correct 3D coordinates at each vertex of the surface. Edge swap and multiresolution learning are proposed to make the algorithm more effective and more efficient. The whole algorithm is very simple to implement. Experimental results have shown our techniques are successful.