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Point cloud denoising method combining feature detection method and vertex updating method

A point cloud denoising and update method technology, applied in the field of point cloud denoising, can solve problems such as poor recovery and fragmentation, and achieve the effect of recovering sharp features

Active Publication Date: 2020-09-08
CHINA UNIV OF GEOSCIENCES (WUHAN)
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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to provide a method that can simultaneously restore sharp features in view of the defects that the existing technology cannot restore small-scale features well, and a certain degree of fragmentation will occur in nonlinear smooth regions. and Point Cloud Denoising Methods for Nonlinear Smooth Regions

Method used

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  • Point cloud denoising method combining feature detection method and vertex updating method
  • Point cloud denoising method combining feature detection method and vertex updating method
  • Point cloud denoising method combining feature detection method and vertex updating method

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Embodiment 1

[0036] Aiming at the defect that the existing technology cannot restore small-scale features well, and a certain degree of fragmentation phenomenon will occur in the nonlinear smooth area, this embodiment discloses a point cloud denoising method combined with the vertex update method (For the implementation process of this method, please refer to figure 1 ), including the following steps:

[0037] S1. Obtain the position information of the point cloud, the position information of the point cloud includes the vertex {p of the point cloud i :i=1, 2, ..., V}, normal vector information {n i :i=1,2,...,V}, wherein, V represents the number of vertices, and i is the i-th vertex obtained; according to the position information of the point cloud, calculate and store the KNN neighborhood information of each point Specifically, when those skilled in the art obtain the position information of the point cloud, they can use the point cloud algorithm library (VCG) to obtain the vertex and ...

Embodiment 2

[0060] Based on embodiment 1, its difference is that in step S3 (the implementation process of this method please refer to figure 2 ), in step S3, when performing feature classification to the point cloud, including combining the point cloud normal vector, vertex information, and domain information, constructing the tensor information of the point cloud normal vector, and the tensor information of the vertex; wherein:

[0061] Tensor information of the constructed point cloud normal vector for:

[0062]

[0063] in, is the mth eigenvalue The corresponding eigenvectors.

[0064] Tensor information of the constructed vertices for:

[0065]

[0066] in, is the mth eigenvalue The corresponding eigenvectors.

[0067] After constructing the tensor information of the normal vector of the point cloud and the tensor information of the vertices, the feature point detection and classification are performed sequentially to obtain the feature point set. Distinguish fr...

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Abstract

The invention discloses a point cloud denoising method combining a feature detection method and a vertex updating method, and the method comprises the steps: firstly defining a new discrete operator,called an anisotropic second-order operator, on a point cloud, and enabling the new discrete operator to serve as a regular term in optimization to recover a normal vector field of the point cloud; secondly, based on the normal vector field optimized in the previous step, providing a feature point detection method based on bilateral tensor voting, and performing feature point detection and classification; then, utilizing the classification feature points detected in the previous step, and calculating for each feature point based on a RanSAC algorithm to obtain multiple normal vectors; and finally, performing vertex updating by using the multiple normal vector information so as to obtain denoised point cloud data. Compared with the prior art, the point cloud denoising method has the advantages that a nonlinear smooth area can be better recovered while sharp geometrical characteristics are kept, and an ideal denoising effect is achieved.

Description

technical field [0001] The invention belongs to the field of computer graphics processing, and in particular relates to a point cloud denoising method combined with a feature detection method and a vertex update method. Background technique [0002] With the advancement of sensor technology and the rapid development of computer graphics, people have entered a new era of digitalization, accompanied by the emergence of some novel technologies such as 3D animation, 3D printing, virtual reality, and augmented reality. As a new type of three-dimensional data expression, 3D point cloud data has more and more methods to obtain, and it is fast and convenient. Therefore, 3D point cloud processing technology has also become a hot spot in scientific research. , game design, unmanned driving, 3D printing, computer-aided design (CAD), medicine, military, terrain surveying and other fields have been widely used. However, in the process of point cloud acquisition, due to the influence of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06K9/46
CPCG06V10/44G06T5/70Y02T10/40
Inventor 刘郑肖晓文郭明强钟赛尚谢忠
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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