Dissimilarity weighting for graph-based point cloud segmentation using local surface gradients

Ali Saglam, H. B. Makineci, Ö. K. Baykan, N. Baykan
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引用次数: 1

Abstract

Processing of 3D point cloud data is seen as a problem due to the difficulties of processing millions of unstructured points. The point cloud segmentation process is a crucial pre-classification stage such that it reduces the high processing time required to extract meaningful information from raw data and produces some distinctive features for the classification stage. Local surface inclinations of objects are the most effective features of 3D point clouds to provide meaningful information about the objects. Sampling the points into sub-volumes (voxels) is a technique commonly used in the literature to obtain the required neighboring point groups to calculate local surface directions (with normal vectors). The graph-based segmentation approaches are widely used for the surface segmentation using the attributes of the local surface orientations and continuities. In this study, only two geometrical primitives which are normal vectors and barycenters of point groups are used to weight the connections between the adjacent voxels (vertices). The defined 14 possible dissimilarity calculations of three angular values getting from the primitives are experimented and evaluated on five sample datasets that have reference data for segmentation. Finally, the results of the measures are compared in terms of accuracy and F1 score. According to the results, the weight measure W7 (seventh calculation) gives 0.8026 accuracy and 0.7305 F1 score with higher standard deviations, while the original weight measure (W8) of the segmentation method gives 0.7890 accuracy and 0.6774 F1 score with lower standard deviations. BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
基于局部表面梯度的点云分割的不相似度加权
由于处理数百万个非结构化点的困难,三维点云数据的处理被视为一个问题。点云分割过程是一个至关重要的预分类阶段,它减少了从原始数据中提取有意义信息所需的大量处理时间,并为分类阶段产生了一些独特的特征。物体的局部表面倾角是三维点云最有效的特征,可以提供有关物体的有意义的信息。将点采样到子体(体素)中是文献中常用的一种技术,用于获得所需的邻近点组以计算局部表面方向(使用法向量)。基于图的分割方法广泛应用于利用局部曲面方向和连续性属性的曲面分割。在本研究中,仅使用法向量和点群质心两个几何基元来加权相邻体素(顶点)之间的连接。在具有分割参考数据的5个样本数据集上,对从原语中得到的3个角度值定义的14种可能的不相似性计算进行了实验和评估。最后,从准确率和F1分数两方面对测量结果进行了比较。结果表明,权重测度W7(第七次计算)的精度为0.8026,F1分数为0.7305,标准差较高,而分割方法的原始权重测度W8的精度为0.7890,F1分数为0.6774,标准差较低。BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
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