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With the vigorous development of the urban construction industry, engineeringdeformation or changes often occur during the construction process. To combatthis phenomenon, it is necessary to detect changes in order to detectconstruction loopholes in time, ensure the integrity of the project and reducelabor costs. Or the inconvenience and injuriousness of the road. In the studyof change detection in 3D point clouds, researchers have published variousresearch methods on 3D point clouds. Directly based on but mostly basedontraditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are toconvert 3D point clouds into DSM, which loses a lot of original information.Although deep learning is used in remote sensing methods, in terms of changedetection of 3D point clouds, it is more converted into two-dimensionalpatches, and neural networks are rarely applied directly. We prefer that thenetwork is given at the level of pixels or points. Variety. Therefore, in thisarticle, our network builds a network for 3D point cloud change detection, andproposes a new module Cross transformer suitable for change detection.Simultaneously simulate tunneling data for change detection, and do testexperiments with our network.

Research on self-cross transformer model of point cloud change detecter