| title | Unified Panoramic Geometry Estimation via Multi-View Foundation Models |
|---|---|
| authors | Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer, Nando Metzger, Denis Rozumny, Konrad Schindler, Nikolai Kalischek |
| date | 2026-05-25 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.26368 |
| affiliation | Photogrammetry and Remote Sensing Lab of ETH Zurich |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct 3D scene structure not only from multi-view imagery, but even from a single view. A natural extension is 3D reconstruction from panoramas, with the exciting prospect of recovering a full 360-degree scene from a single panoramic image. In this work, we introduce PaGeR (Panoramic Geometry Reconstruction), a framework to lift powerful 3D foundation models designed for perspective imagery to the panorama domain. Our strategy is to start from a pre-trained transformer for 3D reconstruction and turn it into a unified high-performance model that predicts scale-invariant depth, metric depth, surface normals, and sky masks from both perspective and omnidirectional images, in a single forward pass. By keeping architectural changes to a minimum and mixing perspective and panoramic images during training, PaGeR retains the rich 3D prior of the underlying foundation model while learning to also estimate geometrically consistent 360-degree scenes from single panoramas. We extensively test our method in both indoor and outdoor environments and find that it delivers state-of-the-art performance and excellent zero-shot performance across a wide range of scenes. |
cite
@misc{bozic2026unified,
title={Unified Panoramic Geometry Estimation via Multi-View Foundation Models},
author={Vukasin Bozic and Isidora Slavkovic and Dominik Narnhofer and Nando Metzger and Denis Rozumny and Konrad Schindler and Nikolai Kalischek},
year={2026},
eprint={2605.26368},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.26368}
}