Airborne Cross-Source Point Clouds Fusion by Slice-to-Slice Adjustment

Abstract. Point cloud fusion is a process plays pivotal role in geospatial data analysis that aims to integrate data from multiple sources to create a comprehensive and precise representation of the environment. Integrating point clouds acquired from cross-source or hybrid sensors presents unique challenges due to differences in geometric accuracy, precision, and the size of data gaps, along with variations in available attributes. Significant progress has been made in developing algorithms and methods to address these challenges, but the problems are not sufficiently resolved and remain one of the most challenging aspects of geospatial data processing. In this paper, we present a new approach for airborne cross-source point cloud fusion through a slice-to-slice adjustment. Our method generates cross-sectional slices and aligns them following some sequential steps. This approach enhances the accuracy and completeness of the fused point cloud, overcoming issues related to geometric disparities and data gaps. Experimental results demonstrate the effectiveness of our approach in improving registration accuracy, preserving geometric detail, and providing valuable insights for utilizing the potentials of both data sources.