Building monomer instance segmentation method and device

A building and monomerization technology, which is applied in image analysis, image data processing, instruments, etc., can solve problems such as the inability to achieve monomer segmentation, and achieve the effects of improving modeling stability, model accuracy, and reducing computational complexity

Inactive Publication Date: 2022-04-01
深圳市其域创新科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present invention provide a method and device for building individualized instance segmentation, so as to at least solve the technical problem that the existing technology cannot achieve individualized segmentation

Method used

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  • Building monomer instance segmentation method and device
  • Building monomer instance segmentation method and device

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Embodiment 1

[0059] According to an embodiment of the present invention, a method for segmenting building singulation instances is provided, see figure 1 , including the following steps:

[0060] S101. Denoising and removing outliers from the point cloud generated by the UAV image;

[0061] S102. Flip the image point cloud after denoising and removing outliers, and remove the ground;

[0062] S103. Semantically segmenting the image, and selecting points classified as buildings from the point cloud of the entire image;

[0063] S104. Carry out spatial density clustering to the points of the building, and obtain the point cloud clusters that form each building monomer;

[0064] S105. Process each point cloud cluster to generate a three-dimensional frame of the cluster;

[0065] S106. Perform frame cutting and secondary semantic segmentation on the 3D frame to obtain a complete single building point cloud.

[0066] In the example segmentation method of building individualization in the em...

Embodiment 2

[0125] According to another embodiment of the present invention, there is provided a device for segmenting building singularization instances, see figure 2 ,include:

[0126] The preprocessing unit 100 is used to reduce noise and remove outliers from the point cloud generated by the UAV image;

[0127] The flipping unit 200 is used to flip the image point cloud after denoising and removing outliers, and remove the ground;

[0128] The semantic segmentation unit 300 is used to perform semantic segmentation on the image, and filter out points classified as buildings from the entire image point cloud;

[0129] The spatial density clustering unit 400 is used to perform spatial density clustering on the points of the buildings to obtain the point cloud clusters that make up each individual building;

[0130] The cluster generation unit 500 is configured to process each point cloud cluster cluster to generate a three-dimensional border of the cluster;

[0131] The building point...

Embodiment 3

[0157] A storage medium stores a program file capable of implementing any one of the above-mentioned methods for segmenting individualized building instances.

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Abstract

The invention relates to the technical field of computer vision application, in particular to a building monomer instance segmentation method and device, and the method comprises the steps: carrying out the noise reduction of a point cloud generated by an unmanned plane image, and removing outliers; overturning the image point cloud after noise reduction and outlier removal, and removing the ground; semantic segmentation is carried out on the image, and points of which the categories are buildings are screened out from the point cloud of the whole image; performing spatial density clustering on the points of the building to obtain a point cloud cluster family forming each building monomer; processing each point cloud cluster family to generate a three-dimensional frame of the family; and carrying out frame cutting and secondary semantic segmentation on the three-dimensional frame to obtain a complete building point cloud monomer. The method and the device support single segmentation of point clouds generated at lower cost, and have strong robustness. The modeling stability is greatly improved by using the ground filtering algorithm, the calculation complexity is greatly reduced, the operation speed is accelerated, and the model is more accurate by using the deep learning method.

Description

technical field [0001] The present invention relates to the technical field of computer vision applications, in particular to a method and device for segmenting individualized building instances. Background technique [0002] With the huge demand for smart cities and digital twin applications, large-scale urban geographic point cloud data, including topography, buildings, road vegetation, etc., has become an important visual expression carrier. The three-dimensional, hierarchical, and multi-information characteristics of 3D point cloud data make it have advantages that traditional images do not have in displaying the geometric shape, attributes, spatial location, and texture of the city. Changes in the urban landscape, land use and digital cities will play a greater role. [0003] Among them, the 3D model in the digital city system is a model lacking semantic information or a model created by manual monomer segmentation, which cannot meet the needs of real digital city cons...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/10G06T5/00G06T7/66G06V10/762
Inventor 黄印煌陈乾
Owner 深圳市其域创新科技有限公司
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