PGGT-Loc introduces a Primitive‑Grounded Geometry Transformer (PGGT) that performs feed‑forward 6‑DoF camera localization by grounding a single query image to planar map primitives. The method trains a network to predict multiple localization targets, enabling generalization to unseen environments. It achieves state‑of‑the‑art performance on both dense surface maps and minimal layout models using a straightforward, scalable architecture.
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
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.
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.
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.
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.
arXiv:2510.18714v3 Announce Type: replace Abstract: This paper addresses metric 3D reconstruction of indoor scenes by exploiting their inherent geometric regularities with compact representations. Us...
The paper presents MegaEvent, an event‑based visual place recognition system that remains robust to viewpoint changes. By converting five large‑scale geo‑tagged datasets into synthetic event streams and fine‑tuning a vision transformer with a multi‑loss function, MegaEvent achieves an average Recall@1 of 82% on three event‑based localization datasets, outperforming existing methods by 20 recall points. The authors also introduce the Springfield‑Event‑VPR dataset, a 3.7 km walking route recorded in three camera orientations, where MegaEvent surpasses the strongest baseline by 9 recall points.
PointGauss is a 3D-native framework that performs semantic parsing and instance segmentation on 3D Gaussian splatting representations by treating Gaussian primitives as unstructured point sets and extracting scale‑invariant geometric features with Point Transformer V3. It introduces an adaptive region‑of‑interest cropping strategy and an instance‑aware distance‑constrained rasterization pipeline to enable scalable, view‑consistent pixel‑level projections. The authors also release SplatSeg‑360, a cross‑scale benchmark with 32 complex scenes and over 6,300 aligned 2D‑3D masks, and show that PointGauss achieves real‑time performance with state‑of‑the‑art 3D‑mIoU (~90%) and 2D‑mIoU (~80%) scores.
The paper presents a framework that builds a static point cloud prior map from past camera traversals, augmenting each point with DINOv3 semantic features. During runtime, a local prior patch is retrieved, encoded with a sparse voxel backbone, and fused with lifted multi‑view camera features in bird’s‑eye view. This fused representation is then used by sparse transformer heads to predict 3D objects and vectorized map elements, achieving improved performance on Argoverse 2 without requiring LiDAR for prior‑map construction or online inference.
arXiv:2608.13147v2 Announce Type: replace Abstract: Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera str...
arXiv:2608.23290v1 Announce Type: new Abstract: Accurate visual localization on robotic and wearable platforms remains challenging in dense urban environments. Existing methodologies typically rely o...