StratoSplat: Taming Layered Regularities for Sparse Aerial 3D Gaussian Splatting

3D Gaussian Splatting has revolutionized novel view synthe-sis with real-time rendering and photorealistic quality. However, standard3DGS methods fail in aerial scenes due to sparse anisotropic UAV captures.Two critical issues emerge: incomplete triangulated points in texturelessregions that destabilizes Gaussian initialization and view-aligned degen-eracies where Gaussians overfit limited observations rather than respecttrue geometry. We present StratoSplat, a framework that exploits the in-herent layered structure of aerial scenes for robust sparse-view 3DGS. Ourkey insight is that buildings, terrain, and roads naturally stratify alonggravity, providing strong geometric priors to regularize this problem. Weaddress point sparsity through layered memory guided initialization thatenforces multi-view consistency. To prevent optimization degeneracies, weintroduce neural multi-plane Gaussians where virtual primitives anchor tolearned parallel planes for geometric regularization. Extensive experimentsdemonstrate state-of-the-art performance, surpassing the best baseline byover 3dB. Code available at https://github.com/keloee/StratoSplat.