HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion

Recently, cross-source point cloud registration from different sensors has become a significant research focus. Although current methods have advanced homogenous point cloud registration, challenges persist in the cross-source domain due to varying point cloud densities from different sensors and missing points caused by different viewing angles, which have hindered its development. To address these issues, we propose HybridFusion, a novel algorithm designed specifically for cross-source point cloud registration in outdoor large-scale scenes, accommodating various sensors and viewing angles. Due to the unique characteristics of point clouds, it is not a singular module, but rather a coarse-to-fine process. To extract similarity information from cross-source point clouds, local patches of the point cloud are subjected to similarity matching. Subsequently, precise alignment is performed using their distinctive features, including 2D boundary points. Finally, the poses obtained from multiple patches are fused to achieve the final registration. Our proposed approach is extensively evaluated through both qualitative and quantitative experiments with existing methods. Additionally, a novel metric for point cloud completion is introduced. The results establish our method as the state-of-the-art solution for cross-source point cloud registration, with a remarkable 70% increase in accuracy compared to recent approaches.