KEYWORDS: 3D image processing, 3D image reconstruction, 3D modeling, Point clouds, LIDAR, 3D acquisition, Reconstruction algorithms, Image segmentation, Image processing, Buildings
In complex outdoor environments, processing a large amount of point cloud data and restoring the precise structure of real scenes is a key difficulty in real scene reconstruction. To this end, a laser 3D real scene reconstruction and lightweight access method based on LiDAR point cloud is proposed. Using region growing algorithm to segment laser foot points of buildings in 3D reality and obtain LiDAR point cloud data of 3D reality. Using the Euler Lagrange equation to establish a 3D data field for laser 3D real scene image reconstruction, and completing the 3D reconstruction of laser 3D real scene images. Using SSD lightweight algorithm to perform regression classification on the extracted target features, in order to locate targets of different scales and achieve lightweight access to 3D real-world image targets. The experimental results show that the research method can accurately reconstruct three-dimensional real-life buildings with lower reconstruction errors.
Due to the large amount of airborne multispectral light detection and ranging (MS LiDAR) point cloud data, it is required to annotate it to complete supervised learning. However, the annotation cost of large-scale point clouds is high, which can easily lead to incomplete or inaccurate annotation, affecting the accuracy of point cloud classification. Therefore, this article proposes a new weakly supervised MS LiDAR point cloud classification method based on kernel point convolutional semantic query network. Firstly, using kernel convolutional semantic query network to detect weak targets in point clouds. On this basis, sparsify the point cloud data. Introduce weakly supervised learning methods to classify MS LiDAR point clouds. The experimental results have verified that the research method can accurately classify different types of point cloud data, and the time consumption can be controlled within 5ms. Compared with traditional methods, it has significant application advantages.
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