Research on self-cross transformer model of point cloud change detecter
Xiaoxu Ren Haili Sun Zhenxin Zhang
With the vigorous development of the urban construction industry, engineering
deformation or changes often occur during the construction process. To combat
this phenomenon, it is necessary to detect changes in order to detect
construction loopholes in time, ensure the integrity of the project and reduce
labor costs. Or the inconvenience and injuriousness of the road. In the study
of change detection in 3D point clouds, researchers have published various
research methods on 3D point clouds. Directly based on but mostly based
ontraditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to
convert 3D point clouds into DSM, which loses a lot of original information.
Although deep learning is used in remote sensing methods, in terms of change
detection of 3D point clouds, it is more converted into two-dimensional
patches, and neural networks are rarely applied directly. We prefer that the
network is given at the level of pixels or points. Variety. Therefore, in this
article, our network builds a network for 3D point cloud change detection, and
proposes a new module Cross transformer suitable for change detection.
Simultaneously simulate tunneling data for change detection, and do test
experiments with our network.