LOD (level of detail) model quick constructing method for scanning mass scattered point cloud by ground laser in single station

Abstract

The invention discloses an LOD (level of detail) model quick constructing method for scanning mass scattered point cloud by ground laser in a single station. The method comprises the following steps: projecting laser scanned point cloud of an original single station to a spherical surface, constructing a regular spherical grid according to a certain expected resolution, and resampling project data points and color values in the grid to obtain regular point cloud; establishing a topological relationship among the re-sampled points to generate a triangular mesh; and coloring the vertex of the triangle, and deleting unreasonable triangles to obtain a matrix stored true color triangular grid. The LOD model generated by the method has high compression rate, has important influence in the fields of surveying and mapping and reverse engineering, and can be used in simulation roaming.

Description

technical field [0001] The invention relates to a method for quickly constructing a multi-level of detail LOD (Levels of Details) model by utilizing massive scattered point clouds acquired by a ground laser scanner at a single station. Background technique [0002] As a high-tech in the field of surveying and mapping, terrestrial laser scanners have attracted more and more researchers' attention for their fast and high-precision acquisition of three-dimensional geometric data and texture data on object surfaces. The data acquired by most ground-based laser scanners has the characteristics of large data volume, no topological relationship, and uneven density, so it is called massive scattered point cloud. [0003] Due to the complexity of the surface reconstruction problem of massive scattered point clouds, various surface reconstruction algorithms that are currently popular have certain limitations: [0004] (1) Some methods require the density of the point set to be as uni...

AI Technical Summary

Problems solved by technology

[0003] Due to the complexity of the surface reconstruction problem of massive scattered point clouds, various surface reconstruction algorithms that are currently popular have certain limitations:

[0005] (2) Some methods cannot reconstruct solid models containing holes;

[0006] (3) Many methods are not highly automated, requiring users to adjust parameters or assist in selecting seed points;

[0007] (4) The time complexity of most methods is too high and the amount of calculation is large, so the practicability is limited

[0009] (1) In the process of triangulation, every point in the collected point set is used, and the original point cloud is not compressed and simplified, and the amount of calculation is still very large

[0010] (2) It is considered that the points in the point cloud are all on the surface of the object, and the influence of noise points is not considered

[0011] (3) It cannot handle complex topological situations, such as the situation with holes and isolated islands;

[0012] Therefore, this invention is not suitable for massive, dense, and scattered point cloud data containing more noise points, and the obtained triangulation only satisfies the Delaunay criterion in the spherical sense, and its applicability is limited due to the above defects

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