Neighbor Reweighted Local Centroid for Geometric Feature Identification
Neighbor Reweighted Local Centroid for Geometric Feature Identification
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DOI：
10.1109/tvcg.2021.3124911
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发表时间：
2021-11
影响因子：
5.2
通讯作者：
Tong Liu;Zhenhua Yang;Shaojun Hu;Zhiyi Zhang;Chunxia Xiao;Xiaohu Guo;Long Yang
中科院分区：
文献类型：
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作者：
Tong Liu;Zhenhua Yang;Shaojun Hu;Zhiyi Zhang;Chunxia Xiao;Xiaohu Guo;Long Yang
从采样曲面中识别几何特征是一项重要而基础的工作。现有的基于曲率的脊和谷特征识别方法一般对噪声敏感。在不需要高阶微分算子的情况下，大多数基于几何的方法牺牲了一定程度的特征描述能力来换取鲁棒性。然而，这两种类型的方法都不能同时处理表面边界特征。本文提出了一种新的邻域重加权局部质心（NRLC）计算算法来识别点云模型的几何特征。该方法通过将点的相邻向量分解为两个正交方向，构造了点的特征描述子。相邻向量从所考虑的点开始，并以相应的邻居结束。然后，将分解的相邻向量与不同的权重累加以生成NRLC。利用所定义的非线性特征识别器，我们为每个候选特征点设计了一个概率集，从而可以同时识别凸、凹和表面边界点。此外，我们引入了一对特征算子，包括同化和异化，以进一步加强所识别的几何特征。最后，我们测试NRLC的点云模型来自不同的数据源的大机构。通过多组对比实验，验证了该方法的有效性。
Identifying geometric features from sampled surfaces is a significant and fundamental task. The existing curvature-based methods that can identify ridge and valley features are generally sensitive to noise. Without requiring high-order differential operators, most statistics-based methods sacrifice certain extents of the feature descriptive powers in exchange for robustness. However, neither of these types of methods can treat the surface boundary features simultaneously. In this paper, we propose a novel neighbor reweighted local centroid (NRLC) computational algorithm to identify geometric features for point cloud models. It constructs a feature descriptor for the considered point via decomposing each of its neighboring vectors into two orthogonal directions. A neighboring vector starts from the considered point and ends with the corresponding neighbor. The decomposed neighboring vectors are then accumulated with different weights to generate the NRLC. With the defined NRLC, we design a probability set for each candidate feature point so that the convex, concave and surface boundary points can be recognized concurrently. In addition, we introduce a pair of feature operators, including assimilation and dissimilation, to further strengthen the identified geometric features. Finally, we test NRLC on a large body of point cloud models derived from different data sources. Several groups of the comparison experiments are conducted, and the results verify the validity and efficiency of our NRLC method.