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适应性聚类用于点云

Zitao Lin¹, Chuanli Kang^1,2, Siyi Wu¹

¹College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)

|February 10, 2024

概括

本研究介绍了一种适应性聚类方法,用于对大规模点云数据进行细分. 强大的方法有效地对地面物体和噪声进行细分,在实际应用中优于现有的方法.

科学领域:

地理空间科学是一个科学领域.
计算机视觉计算机视觉计算机视觉
机器人技术机器人技术机器人技术

背景情况:

点云细分对于遥感和3D建模等应用至关重要.
现有的方法很难有效地对大规模场景进行细分.
目前点云细分的局限性阻碍了实际应用.

研究的目的:

为大规模点云数据提出适应性聚类细分方法.
为了提高点云细分的效率和稳定性.
在复杂场景中解决当前细分技术的局限性.

主要方法:

采用了自适应集群方法,使用相邻点特征计算值.
细分结果使用集群点的标准偏差进行细化.
代细分是对不符合预定义条件的集群进行的.

主要成果:

与其他技术相比,拟议的方法显示出卓越的实用性和效率.
在公园场景中实现了地面物体和地面点云数据的有效细分.
该方法显示出强大的稳定性,受参数变化的影响较小.

结论:

适应集群方法为大规模点云细分提供了强大而高效的解决方案.
该技术成功地对各种场景元素进行细分,并区分噪声点.
该方法在公共数据集上得到验证,显示了广泛的适用性和良好的性能.

关键词:

这是一个大规模的点云.点云集群点云集群是指点云的集群.点云细分分点云细分

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