Robust moving least-squares fitting with sharp features
Robust moving least-squares fitting with sharp features
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DOI：
10.1145/1073204.1073227
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发表时间：
2005-07-01
影响因子：
6.2
通讯作者：
Silva, CT
中科院分区：
文献类型：
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作者：
Fleishman, S;Cohen-Or, D;Silva, CT
我们引入了一种稳健的移动最小二乘技术，用于从潜在噪声的点云中重建分段平滑表面。我们使用稳健统计技术来指导移动最小二乘 (MLS) 计算所使用的邻域的创建。这导致了一种概念上简单的方法，它提供了一个统一的框架，不仅可以处理噪声，还可以对具有尖锐特征的表面进行建模。我们的技术基于一种用于异常值检测的新的稳健统计方法：前向搜索范例。使用这种强大的技术，我们将点集的区域局部分类为多个无异常值的平滑区域。这种分类允许我们将点投影到局部平滑区域而不是到处都平滑的表面上，从而定义分段平滑表面并提高投影算子的数值稳定性。此外，通过将不连续点上的点视为异常值，我们能够定义尖锐的特征。我们的方法的优点之一是它在表面拟合阶段自动忽略异常值。
We introduce a robust moving least-squares technique for reconstructing a piecewise smooth surface from a potentially noisy point cloud. We use techniques from robust statistics to guide the creation of the neighborhoods used by the moving least squares (MLS) computation. This leads to a conceptually simple approach that provides a unified framework for not only dealing with noise, but also for enabling the modeling of surfaces with sharp features.Our technique is based on a new robust statistics method for outlier detection: the forward-search paradigm. Using this powerful technique, we locally classify regions of a point-set to multiple outlier-free smooth regions. This classification allows us to project points on a locally smooth region rather than a surface that is smooth everywhere, thus defining a piecewise smooth surface and increasing the numerical stability of the projection operator. Furthermore, by treating the points across the discontinuities as outliers, we are able to define sharp features. One of the nice features of our approach is that it automatically disregards outliers during the surface-fitting phase.