KEYWORDS: Clouds, 3D modeling, Reconstruction algorithms, 3D metrology, Data modeling, Reverse modeling, RGB color model, 3D acquisition, Surface plasmons, Optical filters
In this paper, a novel method for measuring the projected area of complex 3D objects based on lidar point cloud data is present. To solve the problem of partial data missing in the process of collection, a method of combining Moving Least Squares (MLS) and greedy projection triangulation algorithm for 3D surface reconstruction is proposed. Combined with the MLS method, the problem of greedy projection triangulation method that it requires the point cloud density to change uniformly is made up. The surface data obtained by this method is smoother and the number of holes is reduced, so that the final projected area calculated is much more accurate. The point cloud display platform is written in C++ under Win10 environment. We select PCL to render point clouds and grids and use VTK framework to implement visual interface, which can display the algorithm results of this article.
In this paper, we propose a non-contact real-time centroid measurement method for non-cooperative targets, which calculates the coordinates of the centroid point of the motion model in real time at a frequency of 30 Hz. The TOF (Time of flight) camera configuration, working principle and point cloud preprocessing technology including point cloud fusion of the measurement system are presented. Through the XYZ coordinates of the point cloud data, we calculate the centroid point position of the model. Experiments show that the measured centroid point XYZ three-axis accuracy is about 0.1mm.
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