| title | Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning |
|---|---|
| authors | Antoine Lorentz, Stéphane May, Valentine Bellet, Dawa Derksen, Bastien Nespoulous |
| date | 2026-09-25 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.31199 |
| affiliation | Thales |
| upvotes | 12 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Large-scale Digital Surface Models (DSMs) can be produced cost-effectively from satellite images via stereo-photogrammetry. However, the resulting 3D maps are often contaminated by noise, outliers, and voids. On the other hand, aerial LiDAR provides high-accuracy elevation measurements at a substantially higher cost. In this work, we study diffusion models conditioned both on photogrammetric DSMs and Pléiades imagery to refine vertically co-registered DSMs. We introduce a modified Stable Diffusion 3 architecture with a pruned text stream and a patch-wise normalization strategy, enabling stable training on LiDAR data and transfer from natural images to elevation maps. Experiments in French cities demonstrate that multimodal conditioning improves elevation accuracy, reducing Dense Urban RMSE from 6.00 to 3.45 m in the in-context cities and from 4.16 to 2.77 m in the held-out city of Bordeaux. |
cite
@misc{lorentz2026enhancing,
title={Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning},
author={Antoine Lorentz and Stéphane May and Valentine Bellet and Dawa Derksen and Bastien Nespoulous},
year={2026},
eprint={2609.31199},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.31199}
}