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针对基于LiDAR点云数据进行建筑物自动重建中存在的数据冗余问题,该文设计了一种定量描述激光点位于地物边缘区几率大小的指标——边缘系数,并据此提出了基于边缘系数的建筑物LiDAR点云数据简化方法。该方法利用激光点与其邻域点的位置、数量及分布计算该点的边缘系数,通过试验分析确定边缘系数的阈值并对点云数据进行分割,最后保留建筑物边缘区域的点,实现点云数据的简化。实验表明,该方法在对点云数据进行高效压缩的同时有效保留了位于地物边缘处的点云,有助于提高海量点云数据处理能力和建筑物重建效率。
Aiming at the problem of data redundancy existing in building automatic reconstruction based on LiDAR point cloud data, this paper presents an index - edge coefficient that quantitatively describes the probability of the laser point being located in the edge region of the feature. Based on this, Coefficient Building LiDAR Point Cloud Data Reduction Method. The method uses the location, quantity and distribution of the laser point and its neighboring points to calculate the edge coefficient of the point, determines the threshold of the edge coefficient by experiment and divides the point cloud data, and finally preserves the point of the edge of the building, Data simplification. Experiments show that this method effectively preserves the point cloud at the edge of the object while effectively compressing the point cloud data, which helps to improve the processing power of massive point cloud and the building reconstruction efficiency.