Two-Stage Gaussian Splatting Optimization for Outdoor Scene Reconstruction

Abstract: Outdoor scene reconstruction remains challenging due to the stark contrast between well-textured, nearby regions and distant backgrounds dominated by low detail, uneven illumination, and sky effects. We introduce a two-stage Gaussian Splatting framework that explicitly separates and optimizes these regions, yielding higher-fidelity novel view synthesis. In stage one, background primitives are initialized within a spherical shell and optimized using a loss that combines a background-only photometric term with two geometric regularizers: one constraining Gaussians to remain inside the shell, and another aligning them with local tangential planes. In stage two, foreground Gaussians are initialized from a Structure-from-Motion reconstruction, added and refined using the standard rendering loss, while the background set remains fixed but contributes to the final image formation. Experiments on diverse outdoor datasets show that our method reduces background artifacts and improves perceptual quality compared to state-of-the-art baselines. Moreover, the explicit background separation enables automatic, object-free environment map estimation, opening new possibilities for photorealistic outdoor rendering and mixed-reality applications.