Cube-Splat: High-Fidelity 360° Gaussian Splatting SLAM via Cubemap Factorization and Adjoint-Consistent Optimization

Recent progress in 3D Gaussian Splatting (3DGS) has en-abled dense visual SLAM with pinhole cameras, yet most pipelines arenot designed for panoramic imagery. We present Cube-Splat, the firstpanoramic GS-SLAM framework that factorizes each 360◦ frame into acubemap of four fixed-orientation virtual pinhole views sharing a singleoptical center. By designating the front face as the primary pose state, weaccumulate gradients from all faces via an adjoint mapping, thereby en-abling multi-face observations to coherently update a single state whilestrictly preserving cross-view geometric consistency. Concurrently, ourmapping module densifies and optimizes anisotropic Gaussians using ag-gregated cubemap rays for high-fidelity, dense reconstruction. Further-more, to rigorously evaluate panoramic SLAM under diverse and chal-lenging conditions, we introduce SynPano, a highly scalable, photoreal-istic synthetic dataset featuring parameterized complex trajectories andmulti-modal ground truth. Extensive evaluations on two public bench-marks (PALVIO and OmniBlender) and our SynPano dataset, collec-tively encompassing both indoor and outdoor scenes, demonstrate thatCube-Splat achieves state-of-the-art (SOTA) performance in tracking ac-curacy and reconstruction fidelity. Both the source code and the SynPanodataset are available at https://github.com/guoxf304/CubeSplat.