arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
By Liyao Wang, Ruipu Wu, Haojun Xu, Lei Shi, Linjiang Huang, Si Liu
Georeferenced 3D reconstruction from ground images requires recovering not only the local scene geometry, but also localizing every camera center and 3D point in a geographic coordinate frame. Existin...
The paper introduces X‑VGGT, a feed‑forward cross‑view geometry transformer that jointly processes ground‑level images and a georeferenced satellite image to predict ground‑view camera poses, dense 3D point maps, and a scene‑level transform (gravity, scale, yaw, translation). This transform projects the local reconstruction into the satellite plane, achieving geo‑registration of all ground images with the satellite image. The authors also propose an evaluation protocol and demonstrate that X‑VGGT outperforms prior cross‑view geo‑localization and multi‑view 3D reconstruction models across multiple datasets.
By David G. Shatwell, Mubarak Shah
The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.
By Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Pascal Fua
OmniVLoc is a new visual localization system designed for omnidirectional cameras, leveraging a topo‑metric framework to overcome field‑of‑view limitations of traditional methods. It combines state‑of‑the‑art global place recognition with monocular depth estimation, solving a scale‑aware pose graph and applying precise spherical bundle adjustment to recover metric‑level poses. A hierarchical verification mechanism filters erroneous outputs, enhancing robustness, and extensive experiments on public and custom datasets demonstrate its superior accuracy and reliability.
By Zhuo Yu, Shuyang Zhang, Huihua Zhu, Wenjia Ouyang, Zuo Zuo, Jianhao Jiao, Yilong Zhu, Xiaopin Zhong, Zongze Wu
ARC‑Loc introduces a new cross‑view localization method that bypasses heavy Bird’s‑Eye‑View transformations and external depth models. By converting ground keypoints into azimuthal rays on a satellite map and exploiting their convergence at the user’s location, the approach uses a minimal Azimuthal Ray Convergence solver and an ARC loss to directly match ground and satellite images. Experiments on VIGOR and KITTI show that ARC‑Loc achieves competitive accuracy while offering faster, memory‑efficient inference and easy integration with existing frameworks.
By Hyeongsik Kim, Mincheol Kim, Heejoon Moon, Je Hyeong Hong