激光雷达
概率逻辑
点云
云计算
计算机科学
点(几何)
遥感
环境科学
人工智能
地理
数学
几何学
操作系统
作者
Ao Hu,Kaijie Xu,Xukun Yin,Di Wang
标识
DOI:10.3389/fphy.2024.1471077
摘要
With the continuous progress of information acquisition technology, the volume of LiDAR point cloud data is also expanding rapidly, which greatly hinders the subsequent point cloud processing and engineering applications. In this study, we propose a point cloud simplification strategy utilizing probabilistic membership to address this challenge. The methodology initially develops a feature extraction scheme based on curvature to identify the set of feature points. Subsequently, a combination of k-means clustering and Possibilistic C-Means is employed to partition the point cloud into subsets, and to simultaneously acquire the probabilistic membership information of the point cloud. This information is then utilized to establish a rational and efficient simplification scheme. Finally, the simplification results of the feature point set and the remaining point set are merged to obtain the ultimate simplification outcome. This simplification method not only effectively preserves the features of the point cloud while maintaining uniformity in the simplified results but also offers flexibility in balancing feature retention and the degree of simplification. Through comprehensive comparative analysis across multiple point cloud models and benchmarking against various simplification methods, the proposed approach demonstrates superior performance. Finally, the proposed algorithm was critically discussed in light of the experimental results.
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