SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911538214862848 |
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| author | Wagner, Valentin Bullinger, Sebastian Arens, Michael Stiefelhagen, Rainer |
| author_facet | Wagner, Valentin Bullinger, Sebastian Arens, Michael Stiefelhagen, Rainer |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_21931 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery Wagner, Valentin Bullinger, Sebastian Arens, Michael Stiefelhagen, Rainer Computer Vision and Pattern Recognition 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. |
| title | SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.21931 |