A laser point cloud reduction method based on dynamic grid k-neighbor search
A technology of neighborhood search and dynamic grid, applied in image analysis, instrumentation, calculation, etc., can solve problems such as cumbersome process, consumption of computer memory space, etc., and achieve the effect of simple algorithm, fast speed, and improved efficiency
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[0055] like figure 1 As shown, the present invention relates to a laser point cloud simplification method based on dynamic grid k-neighbor search, the method is applied to the reconstruction of a three-dimensional model of a physical object from laser point cloud data, and the method includes the following steps:
[0056] S1: Build a dynamic constrained grid to obtain the k-neighborhood points of the point cloud data points:
[0057] 101) Taking a certain data point in the point set as the center, expand the distance of l / 2 along the positive and negative directions of the x, y, and z axes to form a cube with a side length of l;
[0058] 102) In the extended cube range, find the number of points m in this range;
[0059] 103) If m≥αk, go to step 104), otherwise, go to step 105);
[0060] 104) If m≤βk, go to step 106), otherwise, l=l-Δl, reduce the range of the cube, go to step 102), where α and β are adjustment coefficients, when the measurement points are evenly distributed...
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