SplatAnywhere: 3D Gaussian Splatting from Sparse and Inconsistent Street Panoramas

3D Gaussian Splatting (3DGS) produces photorealistic scene models, yet reconstructing street scenes still requires dedicated capture for every target site, making reconstruction costs grow with every new location. Google Street View (GSV) already covers a large share of the world's roads with panoramas captured repeatedly over the years. Reconstruction from GSV would enable worldwide 3D digital coverage at no additional capture cost. However, GSV panoramas are sparse in camera centers, which leaves depth weakly constrained and yields models that look acceptable at training viewpoints but show floating artifacts under extrapolated views. They are also inconsistent in appearance across capture sessions, which adds photometric error that no shared color can remove. Standard photometric optimizers, ill-suited for this problem, compensate for the photometric error with wrong geometry. To reconstruct robustly from GSV, we present SplatAnywhere, an optimization-based pipeline that builds structure-aligned 3DGS scenes directly from GSV panoramas. SplatAnywhere adds structural supervision losses in the regions that Structure from Motion under-represents, suppresses splits where the views disagree, reducing the number of Gaussians while improving the geometry, and uses a session appearance adapter that takes up the photometric differences across capture sessions. Across street scenes sampled from five representative cities, SplatAnywhere reaches the best average PSNR, SSIM, and LPIPS compared with 3DGS baselines, lowers their 3D structural error by 26% to 89%, and holds these gains even for extrapolated views.