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arXiv · 2603.21931

SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery

Valentin Wagner·Sebastian Bullinger·Michael Arens·Rainer Stiefelhagen
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Abstract

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.

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Valentin Wagner, Sebastian Bullinger, Michael Arens, Rainer Stiefelhagen. 2026-03-23. SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery. https://arxiv.org/abs/2603.21931

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