Delaunay based shape reconstruction from large data
Delaunay based shape reconstruction from large data
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DOI：
10.5555/502125.502129
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发表时间：
2001-10
期刊：
影响因子：
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通讯作者：
T. Dey;Joachim Giesen;James Hudson
中科院分区：
文献类型：
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作者：
T. Dey;Joachim Giesen;James Hudson
曲面重建为从样本中建模形状提供了一个强大的范例。对于只有几何坐标作为输入的点云数据，基于Delaunay的曲面重建算法在理论和实践上都是非常有效的。然而，对基于Delaunay的方法的一个主要抱怨是，它们速度慢，不能处理大数据。我们扩展了COCONE算法来处理超大型数据。这是第一个基于Delaunay的表面重建算法，可以在一台普通的机器上处理包含超过一百万个样本点的数据。
Surface reconstruction provides a powerful paradigm for modeling shapes from samples. For point cloud data with only geometric coordinates as input, Delaunay based surface reconstruction algorithms are shown to be quite effective both in theory and practice. However, a major complaint against Delaunay based methods is that they are slow and cannot handle large data. We extend the COCONE algorithm to handle supersize data. This is the first reported Delaunay based surface reconstruction algorithm that can handle data containing more than a million sample points on a modest machine.