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Point cloud data denoising method based on k near neighborhood division

A point cloud data, k-nearest neighbor technology, applied in image data processing, image analysis, instruments, etc., can solve problems such as time-consuming and memory consumption, and achieve the effect of improved computing efficiency and simple structure

Inactive Publication Date: 2017-11-24
CHANGAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this method needs to consume a lot of time and memory due to the need to continuously maintain huge topological connection information.

Method used

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  • Point cloud data denoising method based on k near neighborhood division
  • Point cloud data denoising method based on k near neighborhood division
  • Point cloud data denoising method based on k near neighborhood division

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Embodiment

[0080] This paper provides a point cloud data denoising method based on k-nearest neighbor division, which specifically includes the following steps:

[0081] Step 1: Use the cell method to spatially divide the point cloud data. Including the following steps:

[0082] Step 11: Determine the minimum bounding box of the point cloud data: Traverse all the point cloud data, read in the coordinates of the points, find out the maximum and minimum values ​​of the point cloud data in the directions of the three coordinate axes of X, Y, and Z, respectively use x max 、x min 、y max 、y min ,z max ,z min Indicates that the total number of point clouds recorded at the same time is represented by N; with A(x min ,y min ,z min ), B(x min ,y max ,z min ), C(x max ,y max ,z min ), D(x max ,y min ,z min ), E(x min ,y min ,z max ), F(x min ,y max ,z max ), G(x max ,y max ,z max ), H(x max ,y min ,z max ) is a vertex, constructing a cube that can surround all point cl...

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Abstract

The invention discloses a point cloud data denoising method based on k near neighborhood division. The point cloud data denoising method based on k near neighborhood division includes the steps: utilizing a table cell method to perform space division on point cloud data; acquiring the neighborhood information of the point cloud data, and completing point cloud data k near neighborhood searching; calculating the average distance between two points in the point cloud data; calculating the value of the influence of any one point in the point cloud data on the point of the k near neighborhood; traversing the average value of the point cloud data influence values, and then, on the basis, setting a threshold compared with the influence; comparing the set threshold with the value of the point cloud data influence to judge whether the point is a noise point; and removing the noise point, and obtaining the denoised point cloud data. The point cloud data denoising method based on k near neighborhood division combines the k near neighborhood information which is determined through space division of point cloud data by means of the table cell method, of any one point, with the Gauss influence function which is taken as the influence evaluation function, can effectively remove the noise points in the point cloud model, and can maintain the characteristic information of the original model and improve the calculating efficiency at the same time.

Description

technical field [0001] The invention belongs to the field of point cloud data processing, and specifically refers to a method for denoising point cloud data based on k-nearest neighbor division. Background technique [0002] With the application and development of various measurement methods and scanning technologies, people can directly obtain digital information on the surface of objects with high precision and density, and the three-dimensional information acquisition technology has gradually developed and integrated into many application fields. An important means of obtaining 3D information is a 3D laser scanner. The continuous development of 3D scanning technology makes it one of the important means to quickly and accurately establish 3D models. Compared with other traditional measurement technologies, 3D laser scanning technology has fast scanning speed and high precision, and can realize the measurement of complex objects. The data points representing the surface i...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/10028G06T5/70
Inventor 孙朝云李伟赵朝郝雪丽
Owner CHANGAN UNIV
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