A local feature description method based on three-dimensional point cloud

A local feature, three-dimensional point cloud technology, applied in the field of computer vision, can solve problems such as unevenness, sensitivity, noise resolution, etc., to achieve the effect of accelerating feature matching efficiency, strong discrimination and robustness, and saving storage space

Active Publication Date: 2019-01-15
SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
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  • Application Information

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Problems solved by technology

However, the normal vector of each point needs to be estimated, and the estimation of the normal vector is easily affected by noise and uneven resolution, so the final feature descriptor will also be sensitive to these factors.

Method used

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  • A local feature description method based on three-dimensional point cloud
  • A local feature description method based on three-dimensional point cloud
  • A local feature description method based on three-dimensional point cloud

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Embodiment Construction

[0042] The present invention will be further described in detail below with reference to the drawings and embodiments.

[0043] The present invention is oriented to practical engineering applications and proposes a local feature description method based on a three-dimensional point cloud, which can realize the matching of similar parts between two point cloud scenes, and is suitable for complex and changeable background conditions. The present invention includes the following four steps: 1. Extract a number of feature points from the scene point cloud, take each feature point as the center, and establish a three-dimensional local coordinate system according to the points in its spherical neighborhood; 2. The point is transformed into the corresponding local coordinate system, and the spherical neighborhood is divided into the space area along the radial direction; 3. For each divided space area, calculate the relationship between each point located in it and the x-axis and z-axis ...

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Abstract

The invention relates to a local feature description method based on three-dimensional point cloud. The method comprises the following steps: extracting a plurality of feature points from a scene point cloud, taking each feature point as a center, and establishing a three-dimensional local coordinate system according to points in a spherical neighborhood thereof; transforming the points in the spherical neighborhood of the feature points into the corresponding local coordinate system, and partitioning the spatial region of the spherical neighborhood along the radial direction. For each partitioned spatial region, the cosine values alpha and beta of the included angle between each point and the x-axis and z-axis of the coordinate system are calculated and mapped to two independent one-dimensional histograms, respectively. The one-dimensional histograms of all regions are connected in series and then divided by the total number of points in the spherical neighborhood of the feature points to normalize and obtain the final three-dimensional local feature description. The three-dimensional local feature descriptor provided by the invention has the characteristics of good discrimination, strong robustness and high computational efficiency, and improves the correct matching rate of similar parts between scene point clouds.

Description

Technical field [0001] The invention belongs to the field of computer vision, and specifically relates to a local feature description method based on a three-dimensional point cloud. Background technique [0002] Finding similarities in two different point cloud scenes is the basis of many applications, such as 3D scene reconstruction, 3D target recognition, and 3D target retrieval. The process of finding similarities is called feature matching. The feature description of the three-dimensional scene is the premise of feature matching. Due to the limitation of the accuracy of the sensor itself and the different acquisition angles of view, the point cloud data of the acquired scene may have uneven resolution, entrained noise, and even holes in some areas and the situation where the target of interest is blocked. These factors will increase Difficulty of large feature description. How to design a three-dimensional local feature with good distinguishability and strong robustness i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T17/30
CPCG06T17/30
Inventor 朱枫鲁荣荣吴清潇郝颖明范晓鹏付双飞
Owner SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
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