arXiv Computer Vision
GeoLink: A 3D-aware Framework to Improve Generalization for Cross-view Geo-localization
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jun 30
Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
Hugging Face Trending Papers
Aug 10
Warp-free Cross-view Geo-localization via Feature-space Consensus Mining
Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imagery. Although existing methods often use geometric warping to expose co-visible cues, such transformations rely on restrictive spatial assumptions and inevitably introduce severe visual distortions under view-dependent visibility, yielding noisy supervision and fragile correspondences.
arXiv Computer Vision
2d ago
Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding
arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
Hugging Face Trending Papers
Jun 23
Compact Object-Level Representations with Open-Vocabulary Understanding for Indoor Visual Relocalization
Indoor visual relocalization plays a critical role in emerging spatial and embodied AI applications. However, prior research was predominantly devoted to low-level vision schemes, struggling to perceive scene semantics and compositions, which limits both interpretability and applicability.
Hugging Face Trending Papers
Aug 10
GeoUniPR: A Geometry-Consistent Unified Framework for Cross-Modal Place Recognition
Cross-modal place recognition (CMPR) aims to identify the same location across heterogeneous sensing modalities, such as vision and LiDAR. Existing methods commonly bridge the modality gap using complex alignment modules, multi-stage training, or full fine-tuning of pretrained backbones.
arXiv Machine Learning
Jun 29
Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding
arXiv:2604. 02546v2 Announce Type: replace-cross Abstract: Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable representations for 3D scene understanding.