| 基于 FPFH 特征提取的散乱点云精简算法 |
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| 作者姓名： | 李海鹏 徐丹 付宇婷 柳雁安 张婷婷 |
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| 作者单位： | 云南大学信息学院，云南 昆明 650500 |
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| 基金项目： | 国家自然科学基金项目(61761046，62061049，62162068)；云南省“万人计划”云岭学者专项(YNWR-YLXZ-2018-022)；云南省科技厅-
云南大学“双一流”建设联合基金项目(2019FY003012)；云南大学研究生科研创新项目(Y2000211) |
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| 摘 要： | 针对原始点云模型中存在大量冗余数据问题，提出一种基于快速点特征直方图(FPFH)特征提取的点云精简算法，有效兼顾了特征信息保留和整体完整性。算法首先查找并保留原始模型的边缘点；然后计算非边缘点的 FPFH 值，由此得到点云的特征值，并进行排序且划分出特征区域和非特征区域，保留特征区域内的点；最后将非特征区域划分为 k 个子区间，对每个子区间用改进的最远点采样算法进行采样。将该算法与最远点采样算法、非均匀网格法、k-means 算法和自适应曲率熵算法进行对比实验，并用标准化信息熵评价方法对精简后的点云进行评价，实验表明其优于其他精简算法。此外，可视化结果也表明，该算法能够在保证精简模型完整性的同时，较好地保留住点云大部分特征信息。
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| 关 键 词： | 点云精简 快速点特征直方图 最远点采样 边界保留 标准化信息熵 |
A scattered point cloud simplification algorithm based onFPFH feature extraction |
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| Authors: | LI Hai-peng XU Dan FU Yu-ting LIU Yan-an ZHANG Ting-ting |
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| Affiliation: | School of Information Science and Engineering, Yunnan University, Kunming Yunnan 650500, China |
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| Abstract: | To address the large amount of redundant data in the original point cloud, a point cloud simplification algorithm based on fast point feature histograms (FPFH) feature extraction was proposed, effectively taking into account the retention of feature information and overall integrity. Firstly, the algorithm sought and retained the boundary points of the original model. Then, the FPFH value of non-boundary points were calculated, thus producing the feature value of the point cloud. After sorting the feature values, the non-boundary points were divided into the feature region and the non-feature region, retaining the points in the feature region. Finally, the non-feature region was divided into k sub-intervals, and the improved farthest point sampling algorithm was employed to sample each sub-interval. The proposed algorithm was compared with the farthest point sampling algorithm, non-uniform grid method, k-means algorithm, and adaptive curvature entropy algorithm, and the simplified point cloud was evaluated by the standardized information entropy evaluation method. Experimental results show that the proposed algorithm outperforms other simplification algorithms. In addition, the visualization results indicate that the proposed algorithm can not only ensure the integrity of the simplified model but also retain most of the feature information of the point cloud. |
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| Keywords: | point cloud simplification fast point feature histograms farthest point sampling boundary reservation standardized information entropy |
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