Completeness Assessment and Autonomous Resampling of Building Point Clouds with Aerial Priors

Abstract. Based on methods such as airborne oblique photogrammetry and laser scanning, high-precision urban 3D point clouds can be obtained. However, existing airborne 3D data acquisition techniques are prone to interference from dense building occlusions or vegetation cover, making it difficult to capture complete building point clouds. To address this issue, this paper proposes a sky-ground cross-perspective collaborative method for building point cloud completeness detection and autonomous completion. The core idea of this method is to use aerial point clouds as a basis, conducting completeness detection of airborne building point clouds to identify missing regions in both point and surface forms. Subsequently, aerial point cloud priors are employed to guide global and local route planning for ground platforms. Finally, an autonomous completion of building point clouds is achieved through a multi-objective TARE exploration method. The proposed method is evaluated through experiments conducted in both simulation and real-world scenarios. Effectiveness analysis is performed from the perspectives of point cloud completeness and building model reconstruction accuracy. The results show that the proposed sky-ground cross-perspective collaborative point cloud completion method can acquire building point clouds with higher completeness and significantly improve the modeling accuracy of building point clouds.