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

Abstract

The invention provides a 3D point cloud data semantic segmentation method based on deep learning and a self-attention mechanism, which is used for solving the technical problem of low segmentation precision in the prior art, and comprises the following implementation steps of: (1) obtaining a training set and a verification set; (2) constructing a 3D point cloud data semantic segmentation network of deep learning and a self-attention mechanism; (3) setting a loss function required by the 3D point cloud data semantic segmentation network for training deep learning and a self-attention mechanism; (4) carrying out supervised training on the 3D point cloud data semantic segmentation network of deep learning and a self-attention mechanism; and (5) obtaining a semantic segmentation result of the 3D point cloud data test set. According to the method, the self-attention module is added to the deep learning network, the deep features containing the relations between the feature channels can be better extracted, and therefore the segmentation precision is improved.

Description

technical field

[0001] The invention belongs to the technical field of radar 3D point cloud data processing, and relates to a 3D point cloud data segmentation method, in particular to a 3D point cloud data semantic segmentation method based on deep learning and a self-attention mechanism. It can be used in autonomous driving, robotics, 3D maps, land surveying and mapping, foreground segmentation, smart city construction, agricultural production estimation, forest resource census, ecological environment monitoring, disaster prevention and mitigation, etc. Background technique

[0002] In recent years, with the development of depth sensors, point cloud processing has become one of the research hotspots. Point cloud data refers to: scanning data is recorded in the form of points, each point contains three-dimensional coordinates, and some may also contain information such as color information, reflection intensity information, gray value, depth or return times. Generally, it i...

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