Semantic segmentation method of 3D point cloud data based on deep learning and self-attention

A technology for point cloud data and semantic segmentation, applied in image data processing, image analysis, character and pattern recognition, etc., can solve the problem of low segmentation accuracy and achieve the effect of improving accuracy

Active Publication Date: 2021-09-03
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the defects in the above-mentioned prior art, and propose a 3D point cloud data semantic segmentation method based on deep learning and self-attention mechanism, which is used to solve the low segmentation precision existing in the prior art question

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  • Semantic segmentation method of 3D point cloud data based on deep learning and self-attention
  • Semantic segmentation method of 3D point cloud data based on deep learning and self-attention
  • Semantic segmentation method of 3D point cloud data based on deep learning and self-attention

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Embodiment Construction

[0031] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] Refer figure 1 The present invention includes the following steps:

[0033] Step 1) Get the training set R 2 And verification set V 2 :

[0034] Step 1A) Get the 3D point cloud data file with the label from the database f: {f 1 , F 2 , ..., f i , ..., f f ,}, And proportion there is N R 3D point cloud data file as an initial training set R 0 , The rest f (1-N R 3D point cloud data file as an initial verification set V 0 , F i Indicates the i-th 3D point cloud data file, f is the total number of 3D point cloud data file, f ≥ 100, 0.6 ≤ N R R = 0.8, this will randomly select 80% of 3D point cloud data files from the database as the initial training set R 0 , The remaining 20% ​​3D point cloud data files as the initial verification set V 0 ;

[0035] Step 1B) Put R 0 Enter the PDAL library for blocking to get the training data block set R...

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Abstract

The present invention proposes a 3D point cloud data semantic segmentation method based on deep learning and self-attention mechanism, which is used to solve the technical problem of low segmentation accuracy existing in the prior art. The implementation steps include: (1) Obtaining a training set and verification set; (2) construct the 3D point cloud data semantic segmentation network of deep learning and self-attention mechanism; (3) set the loss function required for the 3D point cloud data semantic segmentation network of training deep learning and self-attention mechanism; (4) Perform supervised training on the 3D point cloud data semantic segmentation network with deep learning and self-attention mechanism; (5) Obtain the semantic segmentation results of the 3D point cloud data test set. The present invention adds a self-attention module to the deep learning network, which can better extract deep features including the relationship between various feature channels, thereby improving the segmentation accuracy.

Description

Technical field [0001] The present invention belongs to the field of radar 3D dot cloud data processing, and it is directed to a 3D point cloud data segmentation method, which specifically relates to a semantic semantic semantic semantic semantic division method based on deep learning and self-focus mechanism. Can be used in automatic driving, robotics, 3D maps, land surveying, prospect segmentation, smart city construction, agricultural estimates, forestry resource census, ecological environment monitoring and disaster prevention and mitigation. Background technique [0002] In recent years, with the development of deep sensors, point cloud processing has become one of the research hotspots. Point cloud data refers to: Scanning information is recorded in the form of a point, and each point contains three-dimensional coordinates, and some may contain information such as color information, reflection intensity information, gray value, depth, or the number of returns. Generally, it...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06T7/10
CPCG06T7/10G06T2207/10028G06F18/211G06F18/214
Inventor 焦李成李玲玲张杰张格格马清华郭雨薇丁静怡张梦璇程曦娜王佳宁
Owner XIDIAN UNIV
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