In order to obtain the real ground point cloud, it is necessary to filter the original point cloud data to remove the non ground points. In the subtropical evergreen broad-leaved forest belt, the vegetation is dense, and the ground points are seriously sheltered. The ground points and non ground points are staggered. According to the characteristics of point clouds in the natural belt of the study area, this paper proposes a slope filtering method for dense vegetation terrain point clouds of airborne LiDAR. First, the virtual grid is introduced to segment the point cloud data, and the grid index of each point is calculated; Then calculate the elevation of the lowest point in each grid to calculate the height difference between each point and the lowest point in each grid. Then, the slope values of all points in each grid are obtained, and the local slope statistics are carried out to calculate the slope threshold; Finally, ground point filtering is carried out according to the adaptive slope threshold obtained by preset height difference threshold, k-means clustering and normal distribution. The flat terrain area and undulating terrain area are used for experimental analysis respectively. The results show that this method solves the problem of dense filtering of non surface points, and can not only remove dense vegetation but also retain terrain details.
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.
KEYWORDS: Clouds, Data modeling, 3D modeling, Reconstruction algorithms, Raster graphics, 3D scanning, Local area networks, Laser scanners, Laser applications, Image segmentation
An iterative slicing reconstruction method for point cloud surface holes is proposed to address the problem that the traditional hole repair method fails in repairing surface holes with uneven density. Firstly, the least squares micro-slices are used to detect and extract the point cloud hole boundaries, and then the least enclosing box is constructed and initially rasterized to achieve a uniform segmentation effect. Then the density of segmentation results is analyzed and judged, and if the density is too large, iterative slicing calculation is performed to obtain uniformly dense segmentation blocks. Finally, the moving least squares method is used to fit each slice data to reconstruct the missing part of the point cloud surface. Our results show that this method can achieve the effect of filling the point cloud holes and averaging the point cloud density as well as improving the accuracy of hole repair for holes containing curved surfaces or point cloud data with uneven density.
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