Large-scale point cloud semantic segmentation method based on lightweight neural network

Abstract

The invention discloses a large-scale point cloud semantic segmentation method based on a lightweight neural network. The main body of the method is a semantic segmentation neural network model which comprises a feature extraction network and a feature propagation network. The feature extraction network is used for extracting global features and local features of the three-dimensional point cloud, the feature propagation network propagates the features to original points, and an output feature map corresponds to the probability that each point belongs to each semantic category. According to the method, a non-local module is designed in local and non-local modules, feature learning of the local module is enhanced, network weight parameters are updated by using a focus loss function, the weight of a simple sample is automatically reduced in the training process, and the model is quickly concentrated on a difficult sample. In terms of performance, the training time and the memory space are greatly reduced, and meanwhile, the method has very high point cloud semantic segmentation precision.

Description

technical field [0001] The invention belongs to the field of three-dimensional point cloud and pattern recognition, and the field of deep learning technology, and more specifically relates to a large-scale point cloud semantic segmentation method based on a lightweight neural network. Background technique [0002] Semantic segmentation is one of the most important research techniques in the field of computer vision, which aims to divide each pixel or point in the scene into several regions with specific semantic categories. Semantic segmentation is the basis of 3D scene understanding. It has achieved very good results in the fields of map geographic information, navigation and positioning, computer vision, pattern recognition, etc. It has important research significance and broad application prospects. The 3D point cloud semantic segmentation methods based on deep learning are mainly divided into indirect segmentation methods and direct segmentation methods. The indirect me...

AI Technical Summary

Problems solved by technology

[0003] Aiming at the problem of high computational complexity of the existing 3D point cloud semantic segmentation method, the present invention provides a 3D point cloud semantic segmentation method based on a lightweight neural network, aiming to reduce the time and space of the 3D point cloud semantic segmentation method complexity while retaining the accuracy of point cloud semantic segmentation

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