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

Active Publication Date: 2022-07-01
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

The octree method generally uses a recursive data structure to divide the non-leaf nodes into eight sub-nodes, which consumes a lot of computer memory space
Although the improved self-adaptive octree reduces the storage consumption through one simplification, there are still problems such as the need for numbering between adjacent nodes, which makes the process cumbersome.

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  • A laser point cloud reduction method based on dynamic grid k-neighbor search
  • A laser point cloud reduction method based on dynamic grid k-neighbor search
  • A laser point cloud reduction method based on dynamic grid k-neighbor search

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Embodiment

[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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Abstract

The invention relates to a laser point cloud simplification method based on dynamic grid k-neighborhood search. The method first calculates the curvature of the point, the average value of the normal angle between the point and the neighboring point, and the point from the k-neighborhood of the point cloud data point. The average distance from the neighbor points, and then use the above three parameters to define the feature discrimination parameter and feature threshold, compare the size, extract and retain the feature points, and finally use the bounding box method to simplify the non-feature points twice. The point cloud is spliced ​​with the feature points, and finally the simplified point cloud data is obtained. Compared with the prior art, the method of the present invention can retain the geometric features of the model with high precision, avoid the generation of blank areas, effectively improve the calculation efficiency, and have good practical value.

Description

technical field [0001] The invention relates to a method for reconstructing a three-dimensional model of a physical object from laser point cloud data, in particular to a method for reducing laser point cloud based on dynamic grid k-neighbor search. Background technique [0002] The point cloud data obtained by the existing 3D laser scanners contain rich detailed features and a huge amount of data, including a large number of redundant data points. If the necessary data preprocessing is not performed, it will seriously affect the model reconstruction process. Efficiency, too dense data points will also affect the smoothness of the target surface reconstruction, and even cannot achieve model reconstruction due to the existence of noise points. Therefore, it is a very important and practical work to simplify the point cloud data on the premise of retaining the model features. [0003] In recent years, the reduction methods for scattered point clouds are mainly divided into tw...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T17/10G06T7/64G06V10/77G06V10/74G06K9/62
CPCG06T17/10G06F18/2135
Inventor 陈辉黄晓铭冯燕徐鹏崔承刚
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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