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Extracting feature lines from point clouds based on smooth shrink and iterative thinning

机译：基于平滑收缩和迭代细化从点云中提取特征线

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摘要

An index of measuring the variation on a surface called the smooth shrink index (SSI) which presents robustness to noise and non-uniform sampling is developed in this work. Afterwards, a new algorithm used for extracting the feature lines was proposed. Firstly, the points with an absolute value of SSI greater than a given threshold are selected as potential feature points. Then, the SSI is applied as the growth condition to conduct region segmentation of the potential feature points. Finally, a bilateral filter algorithm is employed to obtain the final feature points by thinning the potential feature points iteratively. While thinning the potential feature points, the tendency of the feature lines is acquired using principle component analysis (PCA) to restrict the drift direction of the potential feature points, so as to prevent the shrink in the endpoints of the feature lines and breaking of the feature lines induced by non-uniform sampling.
机译：在这项工作中，开发了一种测量表面变化的指标，称为光滑收缩指数（SSI），该指数可表现出对噪声和不均匀采样的鲁棒性。然后，提出了一种用于提取特征线的新算法。首先，将SSI的绝对值大于给定阈值的点选择为潜在特征点。然后，将SSI用作生长条件，以对潜在特征点进行区域分割。最后，采用双边滤波算法通过迭代地减薄潜在特征点来获得最终特征点。在细化潜在特征点的同时，使用主成分分析（PCA）来获取特征线的趋势，以限制潜在特征点的漂移方向，从而防止特征线端点的收缩和特征线的断裂。非均匀采样引起的特征线。

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