A method and device for local feature extraction of three-dimensional point cloud

A local feature and three-dimensional point cloud technology, applied in computer parts, instruments, calculations, etc., can solve the problems of inaccurate estimation, easy ambiguity of local features, and the accuracy needs to be improved, so as to achieve the effect of improving accuracy

Active Publication Date: 2019-01-04
PEKING UNIV SHENZHEN GRADUATE SCHOOL
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the three 3D local feature descriptors in the prior art all ignore the concave-convex feature of the point cloud surface, which makes the extracted local features prone to ambiguity, so when applied to the processing of 3D point clouds, the estimation is often inaccurate situation occurs
The accuracy of local feature extraction in the prior art needs to be improved

Method used

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  • A method and device for local feature extraction of three-dimensional point cloud
  • A method and device for local feature extraction of three-dimensional point cloud
  • A method and device for local feature extraction of three-dimensional point cloud

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Embodiment 1

[0027] Please refer to figure 1 , figure 1 It is a flowchart of a method for extracting local features of a 3D point cloud according to an embodiment of the present invention. Such as figure 1 As shown, a local feature extraction method of a 3D point cloud may include the following steps:

[0028] 101. Calculate the angle information between the local feature point to be extracted and the point of each voxel in the preset point cloud sphere, and calculate the concave-convex information of the curved surface between the local feature point to be extracted and the point of each voxel.

[0029] Wherein, the preset point cloud sphere contains several individual elements, and the volume elements are adjacent to the local feature points to be extracted.

[0030] It is worth pointing out that when calculating the angle information between the local feature points to be extracted and the points of each voxel in the preset point cloud sphere, and calculating the concave-convex infor...

Embodiment 2

[0037] The process of this embodiment is basically the same as that of Embodiment 1. The difference is that in this embodiment, before calculating the angle information and concave-convex information, a cloud sphere is first constructed for the local feature points, and the cloud sphere is divided into several adjacent to the local feature points. body elements. Please refer to figure 2 , figure 2 It is a flowchart of a method for extracting local features of a 3D point cloud according to an embodiment of the present invention. Such as figure 2 As shown, this embodiment may include the following steps:

[0038] 201. Construct a point cloud sphere.

[0039] Construct a point cloud sphere with the local feature point to be extracted as the center and the preset length as the radius.

[0040] 202. Segment the point cloud sphere.

[0041] Segment the point cloud sphere along the direction angle, elevation angle and radius of the point cloud sphere to obtain several voxels...

Embodiment 3

[0098] Please refer to Figure 12 , Figure 12 It is a schematic diagram of the device structure of the embodiment of the present invention. Such as Figure 12 As shown, a local feature extraction device of a three-dimensional point cloud may include:

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Abstract

The local feature extraction method and device of the 3D point cloud provided by this application calculates the angle information and concave-convex information between the feature point to be extracted and the point of the adjacent voxel element based on the local reference system corresponding to the point of each voxel element, which can accurately Calculating the feature relationship between two points has the property of translation and rotation invariance, and because the extraction includes the concave-convex information of the local point cloud at the same time, it solves the inaccurate extraction caused by ignoring the ambiguity of concave-convex in the previous 3D local feature description The problem. In the normalization process, the index normalization process and the second normal form normalization process are used to solve the problem of inaccurate similarity calculation caused by a small number of elements in the vector being too large or too small during feature extraction, so that it can Improve the accuracy of the extracted 3D local features.

Description

technical field [0001] The present application relates to a method and device for extracting local features of a three-dimensional point cloud. Background technique [0002] With the rapid development of 3D laser scanning technology, 3D digital geometric model has become the fourth digital media form after digital audio, digital image, and digital video, and its related basic theory and key technology research has developed into a new discipline—— Digital geometry processing has gradually been widely used in computer-aided design, animation and game industry, biomedicine, digital cultural heritage protection and other fields. In addition, with the rise of hardware devices such as Microsoft Kinect and Primesense (a somatosensory technology device), the acquisition of 3D (3Dimensions, three-dimensional) point cloud information has become more convenient and quicker. In 3D vision, local feature extraction has always been the most critical part of point cloud processing, and lo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46
CPCG06V10/50G06V10/44
Inventor 王文敏镇明敏王荣刚李革董胜富王振宇李英高文
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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