A systematic registration method for cross-source point clouds based on cross-view image matching

Abstract

Refined 3D model reconstruction of wide-area cities usually requires registration of multi-source data collected by different platforms and various sensors. Few studies discuss the problem of registration from cross-source image point clouds. This registration task is challenging due to the large variation in the density of point clouds generated from images of different resolutions, the extremely large view differences, the uncertain scale differences of point clouds in arbitrary coordinate systems, and the noise points caused by the low image quality. In this study, we propose a robust point cloud registration method based on cross-view image matching to solve these problems mentioned above. Firstly, the method uses the deep learning cross-view image matching algorithm to obtain 2D image matching points. They are then mapped to 3D space using depth information. Secondly, the dual quaternion is introduced to solve the spatial transformation model. Finally, the ICP fine-registration algorithm is used for optimization. To analyze the performance of the proposed method, experiments are tested on a public dataset in Dortmund, Germany. The experimental results show that the proposed method is not only able to overcome large coordinate system scale differences but is also immune to noise points and outliers. Compared with other point cloud registration methods, it greatly improves the efficiency and accuracy.