Geometry-Propagated Gaussian Splatting for Aerial Sparse Novel View Synthesis

3D Gaussian Splatting achieves impressive novel view syn-thesis for ground-level scenes. However, its performance degrades sig-nificantly in aerial domains due to sparse viewpoint sampling imposedby platform constraints. Existing sparse-view methods typically rely onstructure-from-motion reconstruction, which often yields incomplete ge-ometry due to limited feature correspondences under repetitive texturesand sparse viewpoint overlap. We present Geometry-Propagated Gaus-sian Splatting (GeoProp-GS), which overcomes the limitations of in-sufficient initialization. Our approach introduces two core components:Depth-guided Geometric Initialization (DGI) generates dense point cloudsand extends coverage to unobserved regions; Anchor-constrained Gaus-sian Optimization (AGO) stabilizes under-supervised regions by decom-posing proposal Gaussians into reliable anchors and learnable residuals.Extensive experiments demonstrate that GeoProp-GS achieves state-of-the-art performance and serves as an effective plug-and-play module thatconsistently improves existing methods. Code available at GeoProp-GS .