We present Cross-View Splatter, a feed-forward method that predicts pixel-aligned Gaussian splats for outdoor scenes captured at ground level AND by satellite. Faithful reconstructions require good camera coverage, but ground imagery is time-consuming and hard to capture at scale for large outdoor scenes. Fortunately, satellite imagery can provide a global geometric prior that is easy to access via public APIs. Cross-View Splatter fuses orthorectified satellite views with GPS-tagged ground photos to predict Gaussian splats in a unified 3D coordinate frame. By aligning ground and bird's-eye feature representations, our model improves scene coverage and novel-view synthesis, compared to ground imagery alone. We train on curated georeferenced datasets and paired satellite-terrain data, mined from open mapping services. We evaluate our method on a new benchmark for novel-view synthesis with georeferenced imagery allowing comparison to prior state-of-the-art methods. Our code and data preparation will be available at https://nianticspatial.github.io/cross-view-splatter/.
@article{arxiv.2605.19656,
title = {Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images},
author = {Matias Turkulainen and Akshay Krishnan and Filippo Aleotti and Mohamed Sayed and Guillermo Garcia-Hernando and Juho Kannala and Arno Solin and Gabriel Brostow and Daniyar Turmukhambetov},
journal= {arXiv preprint arXiv:2605.19656},
year = {2026}
}