A Regeneration Algorithm for Extracting Point Cloud Data of Canopy Porosity in Local Landscape
In the process of extracting crown porosity points,a large number of noise points will be generated,leading to the loss of the details of crown porosity point cloud data.In order to ensure the integrity and accuracy of point cloud data,this paper presented an algorithm for extracting and regenerating the crown porosity point cloud data in vernacular landscape.Firstly,3D laser scanning technology was used to obtain the crown point cloud data of the vernacular landscape and calculate the voidage of the crown,and thus divide the point cloud data according to the crown voidage.After that,a regeneration sample of the crown voidage point cloud data was constructed.Then,the im-proved wavelet threshold function and RBF neural network were used to eliminate the noise of point cloud data and fill in the hole of point cloud data,thus achieving the extraction and regeneration of point cloud data.Experimental results show that the proposed method has good extraction and regeneration effect,and only needs 1.002s,indicating that the method has high efficiency.
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