arXiv:2603. 11398v2 Announce Type: replace-cross Abstract: Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability.
arXiv:2610.15062v1 Announce Type: new Abstract: Supporting edge Artificial Intelligence (AI) inference as a network service is challenging because current mobile systems can transport tensors but do...
OpenCVL is a large, open dataset for fine-grained cross-view localization, comprising 617,388 ground‑aerial image pairs from 41 European cities. It blends high‑end sensor data with diverse in‑the‑wild images and includes a curation framework to correct pose annotations, enabling reliable evaluation. The dataset also offers cross‑area and snowy test sets to probe generalization, and experiments show that adding noisy in‑the‑wild data improves model performance on clean tests.
arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.
arXiv:2607. 15713v1 Announce Type: cross Abstract: Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing.
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
arXiv:2606. 01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks.
MIC-BEV is a Transformer-based bird's-eye-view perception framework designed for infrastructure-based multi-camera 3D object detection. It supports a variable number of heterogeneous cameras and employs a graph-enhanced fusion module to adaptively integrate multi-view image features by modeling geometric relationships between cameras and the BEV grid. The authors introduce the M2I synthetic dataset and evaluate MIC-BEV on both M2I and the real-world RoScenes dataset, demonstrating strong detection performance, improved robustness under challenging conditions, and practical feasibility for deployment.
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.
The paper presents a transfer learning–driven approach for indoor localization using visible light communication (VLC) that aims to reduce energy consumption and improve scalability. By applying transfer learning, the method addresses environmental variability such as lighting changes and obstacles, lowering computational overhead compared to conventional techniques. Experiments on a BOSCH factory dataset show a 47% increase in localization accuracy and up to 67.5% model efficiency under high‑noise conditions, outperforming non‑TL models.
arXiv:2607. 03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind and low-vision users.
The paper introduces a transfer learning framework for VLC-based indoor localization that improves cross-area robustness and reduces data needs. Using real measurements from a Bosch facility, fine‑tuning a deep neural network with only 40% of target data recovers 97% of full‑data performance. The authors also propose a Transfer Efficiency Score (TES) to select the best source model, achieving localization errors as low as 40.9 cm and a 3.28 cm mean error on an unseen area.
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.