Graph-GSReg: Leveraging 3D Scene Graphs for Gaussian Splatting Registration

Merging multiple 3D Gaussian Splatting (3DGS) scenes intoa single unified Gaussian representation is essential for large-scale 3Dmapping and long-term map management. Despite its importance, thisarea remains underexplored, and existing solutions exhibit several limi-tations. Learning-based methods attempt direct correspondence betweenGaussian primitives and require training on large 3DGS datasets. Image-based optimization methods depend heavily on coarse initialization fromgeneric foundation models and often incur expensive refinement. Wepresent Graph-GSReg. Our method constructs a 3D scene graph froma 3DGS and its rendered images, reformulating 3DGS registration asa graph registration problem. The proposed 3D scene graph representseach 3DGS at a higher-level representation, enabling a globally consis-tent understanding of semantic information and structural context foraccurate registration. To further construct a seamless unified scene, weintroduce a Self-Supervised Test-Time Optimization. Naively mergingtwo 3D Gaussian scenes often suffers from occlusion artifacts such ashollows and floaters. To alleviate this issue, we refine the merged Gaus-sians to preserve visual consistency between the original scenes and themerged scene. We evaluate our method on real and synthetic bench-marks, demonstrating competitive registration accuracy and merged scenerendering quality.