arXiv Computer Vision
Route-MHT: Multimodal Transformer Guardrails for Thermal Visual Place Recognition
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arXiv Computer Vision
Sep 23
Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition
The paper introduces TFA, a training‑free aggregation technique that calibrates frozen visual foundation models for visual place recognition. TFA uses cross‑codebook agreement, retrieval coverage, and spectral statistics to adjust residual assignment, spectral shaping, and global‑feature fusion without requiring place labels or task‑specific weights. Experiments with a DINOv2‑B backbone show significant Recall@1 gains over existing training‑free methods across multiple benchmarks, demonstrating that reliability‑guided aggregation can unlock additional retrieval performance from frozen representations.
Hugging Face Trending Papers
Sep 22
Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition
Frozen visual foundation models provide transferable features for visual place recognition, but fixed aggregation can suppress useful distinctions in new environments. We introduce TFA, a reliability-...
Hugging Face Trending Papers
Jul 2
DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM
Deep-learning features excel in visual matching, yet their practical value in tightly coupled visual-inertial SLAM (VI-SLAM) remains insufficiently characterized. We present DL-VINS-Factory, a unified framework that integrates learned feature extractors (ALIKED, RaCo, SuperPoint, XFeat) with either Lucas--Kanade (LK) optical-flow tracking or LightGlue (LG) descriptor matching.
arXiv Computer Vision
Sep 15
AG-EgoPose: Spatially Anchored Residual Correction with Action Context for Monocular Egocentric 3D Pose Estimation
arXiv:2603.25175v2 Announce Type: replace Abstract: Monocular egocentric 3D pose estimation is difficult because severe foreshortening, self-occlusion, and a restricted field of view often remove the...
arXiv AI
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CVSD-Reg: Cross-Modal Visual Semantic Prior Distillation for Robust LiDAR Registration
arXiv:2608. 19536v1 Announce Type: cross Abstract: Learning-based global point cloud registration has achieved remarkable progress, yet its reliance on geometric representations makes existing methods sensitive to variations in point density, scan pattern, viewpoint, and sensor characteristics.
arXiv Computer Vision
Sep 21
Multi-viewpoint Geo-localization with Event Cameras
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.