arXiv cs.AI· Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer, Nando Metzger, Denis Rozumny, Konrad Schindler, Nikolai Kalischek·· 4 小时前AI 评分41
PaGeR：基于多视角基础模型的统一全景几何估计
Unified Panoramic Geometry Estimation via Multi-View Foundation Models
AI 导读
研究者提出 PaGeR（Panoramic Geometry Reconstruction）框架，将面向透视图像的 3D 重建基础模型迁移到全景域，可在单次前向传播中从透视和全景图像预测尺度不变深度、度量深度、表面法线和天空掩码。该方法尽量少改架构并混合两类图像训练，保留了基础模型的 3D 先验，在室内外多种场景中达到 SOTA 且零样本表现出色，代码、数据和模型已开源。
正文 · 原文
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. Code, data and models are available $\href{this https URL}{\text{here}}$.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.26368 [cs.CV] |
| (or arXiv:2605.26368v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26368 arXiv-issued DOI via DataCite |
Submission history
From: Vukasin Bozic [view email]
[v1]
Mon, 25 May 2026 22:32:42 UTC (42,439 KB)
[v2]
Wed, 27 May 2026 12:32:56 UTC (42,439 KB)
[v3]
Tue, 10 Nov 2026 16:29:03 UTC (32,849 KB)
来源：arXiv cs.AI · arxiv.org