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.

翻译：随着城市建设行业的蓬勃发展，施工过程中常出现工程变形或变化现象。为应对此问题，需检测变化以便及时发现施工漏洞、保障工程完整性并降低人力成本，或避免道路带来的不便与损害。在三维点云变化检测研究中，研究者们已发表多种三维点云处理方法，但大多基于传统阈值距离方法（如C2C、M3C2、M3C2-EP），部分方法将三维点云转换为数字表面模型（DSM），导致大量原始信息丢失。尽管遥感方法中已采用深度学习，但在三维点云变化检测方面，多将其转换为二维图像块，极少直接应用神经网络。我们更倾向于网络在像素或点层级上感知变化。因此，本文构建了用于三维点云变化检测的网络，并提出一种适用于变化检测的新型模块——交叉变换器（Cross transformer）。同时模拟隧道数据进行变化检测，并利用我们的网络开展了测试实验。