Shared Recoverable Evidence for Ground-Aerial 3D Gaussian Splatting Registration and Fusion

Registration and fusion of independently reconstructed ground and aerial 3D Gaussian Splatting (3DGS) submaps are essential for large-scale scene reconstruction, while cross-platform viewpoint disparities make the task ill-posed. Existing methods overlook the view-dependent radiance of 3DGS primitives, yielding brittle geometric correspondences and severe photometric interference upon naive merging. We present a framework that couples Shared Recoverable Evidence (SRE)-guided registration with camera-conditioned fusion of ground-aerial submaps. SRE-guided registration derives submap-local support from associated training views of 3DGS submaps, then converts it into transform-consistent shared support for Sim(3) proposal refinement and selection. With the transformation fixed, camera-conditioned fusion learns per-Gaussian soft routing selection weights through cross-view map-teacher supervision from frozen submap renders, and modulates opacity to produce one global 3DGS map. Extensive evaluations on ground-aerial pairs from the TerraSky3D, Horizon-GS, and official ScanNet-GSReg benchmarks demonstrate that our approach outperforms existing state-of-the-art methods in both registration accuracy and cross-view rendering fidelity.