We present SatGeo-NeRF, a geometrically regularized NeRF for satellite imagery that mitigates overfitting-induced geometric artifacts observed in current state-of-the-art models using three model-agnostic regularizers. Gravity-Aligned Planarity Regularization aligns depth-inferred, approximated surface normals with the gravity axis to promote local planarity, coupling adjacent rays via a corresponding surface approximation to facilitate cross-ray gradient flow. Granularity Regularization enforces a coarse-to-fine geometry-learning scheme, and Depth-Supervised Regularization stabilizes early training for improved geometric accuracy. On the DFC2019 satellite reconstruction benchmark, SatGeo-NeRF improves the Mean Altitude Error by 13.9% and 11.7% relative to state-of-the-art baselines such as EO-NeRF and EO-GS.
Cite
@article{arxiv.2603.21931,
title = {SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery},
author = {Valentin Wagner and Sebastian Bullinger and Michael Arens and Rainer Stiefelhagen},
journal= {arXiv preprint arXiv:2603.21931},
year = {2026}
}