Abstract
Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.
Highlights
Researchers have proposed many methods for repairing the point cloud with a hole.Ju [1] presented a robust method for repairing arbitrary polygon models
The coarse registration was accomplished by manual selection of points
Holes in laser scanner point cloud data were repaired based on photogrammetry technology
Summary
Researchers have proposed many methods for repairing the point cloud with a hole. Wang et al [10] proposed a method based on the GA-BP neural network for the automatic repair of point cloud holes. The interpolation points are taken as the input data of the GA-BP neural network model, and the predicted values are calculated to complete the repair of point cloud data Optimal density is selected, whose precision results are comparedpoint to those pared those obtained using original point cloud data. This method of repairing obtained using original point cloud data.
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