A 3D point cloud registration method for buildings based on deep learning

Abstract

The invention discloses a three-dimensional point cloud registration method for buildings based on deep learning, comprising the following steps: S1, acquisition of training data; S2, construction of a deep learning model; S3, model training and tuning; S4, to be detected Data preprocessing; S5, detecting key areas of the point cloud; S6, detecting key points of the point cloud; S7, judging the corresponding relationship of key points; S8, calculating the conversion relationship and registration. The present invention applies deep learning to two key steps of point cloud registration: finding key points and determining matching relationships. The present invention makes full use of deep learning's ability to represent point cloud data to find key areas and locate key points step by step. Compared with traditional traversal methods, the detection speed is greatly accelerated, and the deep network model is used to learn point cloud features instead of manual features. , making the algorithm more robust and efficient.

Description

technical field [0001] The invention relates to the field of three-dimensional urban reconstruction and unmanned driving, in particular to a method for registering three-dimensional point clouds of buildings based on deep learning. Background technique [0002] In industrial production, life and scientific research, there is a wide demand for 3D reconstruction, such as AR / VR, 3D printing, automation of industrial production, protection of cultural relics, and application of computer technology in the medical field. Nowadays, the accuracy of 3D scanning instruments is getting higher and higher, and it is becoming easier to obtain fine large-scale urban data. Some developed countries have already started the construction of smart cities, and the 3D reconstruction of cities is an important part of smart cities. For point cloud The research of data registration method becomes the key in the field of 3D reconstruction. [0003] In the traditional point-based registration method,...

AI Technical Summary

Problems solved by technology

This method cannot take into account all the characteristics of the point-based neighborhood, resulting in incomplete feature extraction. Secondly, the traversal method is computationally intensive, especially for the processing of point clouds with large-scale outdoor buildings.

The continuous and rapid development of deep learning at this stage has made major breakthroughs and fruitful scientific research results in many fields, especially in the field of image-related research. just started

Especially in the application of 3D point cloud, the point cloud is basically preprocessed into certain specifications by projecting and dividing into low dimensions, which will cause a large loss of information

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