KEYWORDS: Clouds, Image segmentation, Reconstruction algorithms, Data modeling, 3D scanning, 3D modeling, Nonlinear filtering, Feature extraction, Local area networks, Image processing
Accurate segmentation of building facade point clouds is the key to 3D reconstruction of buildings. The region growing algorithm is widely used because of its simplicity and ease, but the traditional region growing algorithm leads to over-segmentation and under-segmentation problems due to the low robustness of seed points and the large differences in local features of building facade point clouds. To address the above problems, this paper proposes a building facade division based on FPFH feature classification and regional growth. Firstly, Kd-Tree is constructed to spatially index the facade point clouds and construct geometric topological relationships for the cluttered point clouds. Then, FPFH feature values are calculated and sorted for classification, while the points with relatively low feature values are selected as the initial seed points to ensure the stability of the seed points. Finally, the initial seed points are used as the reference for regional growth and face slice segmentation of the building facade point cloud. The experimental results show that the correct rate of the method in this paper is improved by 14.60%, the over-segmentation rate is reduced by 86.20%andtheunder-segmentation rate is reduced by 43.13% compared with the region growing algorithm, which not only improves the over-segmentation and under-segmentation problems, but also increases the segmentation accuracy as well as efficiency.
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