A Segmentation Method of Point Cloud Data Based on Supervoxel

A point cloud data and super-voxel technology, which is applied in the fields of 3D printing and scene understanding, can solve problems such as rough segmentation results and inability to extract segmentation boundaries, so as to improve segmentation efficiency, avoid under-segmentation and over-segmentation problems, and achieve good segmentation results Effect

Active Publication Date: 2019-11-08
XIDIAN UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a point cloud data segmentation method based on supervoxels, aiming to solve the problem that the traditional point cloud segmentation algorithm has rough segmentation results and cannot extract accurate segmentation boundaries

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  • A Segmentation Method of Point Cloud Data Based on Supervoxel
  • A Segmentation Method of Point Cloud Data Based on Supervoxel
  • A Segmentation Method of Point Cloud Data Based on Supervoxel

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Embodiment Construction

[0041] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0042] The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Such as figure 1 As shown, the supervoxel-based point cloud data segmentation method provided by the embodiment of the present invention includes the following steps:

[0044] S101: By considering the three-dimensional geometric relationship and regional connectivity of the point cloud data, the point cloud data is over-segmented by the clustering method to obtain the supervoxels attached to the target boundary;

[0045] S102: Calculate the residual value of the plane fitting of t...

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Abstract

The invention discloses a point cloud data partitioning method based on hyper voxels. The three-dimensional geometrical relationship and regional connectivity of point cloud data are taken into account, the point cloud data are partitioned by using a clustering method, so that the hyper voxels attached on a target boundary are obtained; the residual value in planar fit with data of the hyper voxels is calculated, the hyper voxels are sorted and sieved according to the residual value to obtain effective seed hyper voxels, region growing is carried out by using a normal distribution histogram and the difference between a geodesic distance and an Euclidean distance, and finally partitioning treatment on the point cloud data is finally realized. The point cloud data with indoor local scenes are input, and accurate partitioning for the point cloud data is realized by using the hyper voxels and a region growing algorithm. Compared with a traditional point cloud partitioning method, under the premise of guaranteeing the partitioning efficiency, the problems of insufficient partitioning and over partitioning caused by direct treatment of the point cloud data are avoided, a partitioning result with accurate boundary information is obtained, and the partitioning method is healthy for sampling density and noise of the point cloud data.

Description

Technical field [0001] The present invention belongs to the technical field of 3D printing and scene understanding, and in particular relates to a supervoxel-based point cloud data segmentation method. Background technique [0002] With the widespread application of 3D scanning equipment, especially the popularity of Lidar Scanner (LIDAR) and Microsoft Kinect, 3D point cloud data is easy to obtain and widely used in various fields. Point cloud segmentation is the basic step of 3D point cloud processing. The segmentation results help to accurately locate and identify targets. It has important applications in 3D reconstruction, scene understanding, and 3D printing. Since the point cloud data obtained by scanning is the point cloud data of the indoor local scene, it contains various types of objects. Before modeling 3D printing, it is necessary to separate the point cloud data corresponding to different types of objects to facilitate the individual The objects are modeled and print...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/162
CPCG06T2207/10028G06T2207/20156
Inventor 王泉杨鹏飞田玉敏罗楠姜媛媛
Owner XIDIAN UNIV
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