City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage

Abstract

3D city-scale point cloud stitching is a critical component for large data collection, environment change detection, in which massive amounts of 3D data are captured under different times and conditions. This paper proposes a novel point cloud stitching approach, that automatically and accurately stitches multiple city-scale point clouds, which only share relatively small overlapping areas, into one single model for a larger geographical coverage. The proposed method firstly employs 2D image mosaicking techniques to estimate 3D overlapping areas among multiple point clouds, then applies 3D point cloud registration techniques to estimate the most accurate transformation matrix for 3D stitching. The proposed method is quantitatively evaluated on city-scale reconstructed point cloud dataset and real-world city LiDAR dataset, in which, our method outperforms other competing methods with significant margins and achieved the highest precision score, recall score, and F-score. Our method makes an important step towards automatic and accurate city-scale point cloud data stitching, which could be used in a variety of applications.