arXiv Machine Learning
Jul 7
Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment
arXiv:2607. 01983v1 Announce Type: cross Abstract: Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving.
Multi-Sensor Alignment for Weather Simulations
arXiv:2607. 25612v1 Announce Type: new Abstract: Perception tasks for autonomous vehicles need to work satisfactorily in adverse weather conditions.
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arXiv AI
Jun 3
FRED: A Multi-Modal Autonomous Driving Dataset for Flooded Road Environments
arXiv:2605. 22018v2 Announce Type: replace-cross Abstract: The Flooded Road Environments Dataset (FRED) is, to our knowledge, the first multi-modal autonomous driving dataset specifically targeting the collection of data from scenarios involving water hazards on the road.
arXiv Computer Vision
2d ago
Weather-Aware Domain Adaptation for Street-View Weather Recognition
arXiv:2610.02000v1 Announce Type: new Abstract: Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather...
arXiv Computer Vision
Sep 7
Weather-Conditioned Depth Anything
Weather-Conditioned Depth Anything (DA‑W) is a new framework that enhances monocular depth estimation models, like the Depth Anything series, to perform robustly under adverse weather conditions such as fog, rain, snow, and low‑light. It achieves this by disentangling style from content: a Style Filter extracts weather‑specific embeddings from a curated mix of real and synthetic degradation data, which are then injected into the backbone via a lightweight, zero‑initialized adapter. The adapter is trained with pseudo‑label distillation and alignment, enabling a single unified model to adapt to diverse weather scenarios while preserving its generalization on clean data, and it achieves state‑of‑the‑art performance with an average 3.7% improvement in AbsRel on weather benchmarks.
arXiv Machine Learning
Jun 30
Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse
arXiv:2605. 14925v2 Announce Type: replace-cross Abstract: Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.
arXiv Computer Vision
Sep 23
Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation
The paper presents a foundation-guided auto‑annotation pipeline that improves standard autonomous driving object detectors in adverse weather. By benchmarking YOLOv8, Co‑DETR, and SAM3 on a custom dataset of 25 operational scenarios, the authors find SAM3 to be the most robust and use it offline to generate pseudo‑labels. Fine‑tuning YOLOv8 on these labels boosts overall mAP by 16.04% and yields significant gains in specific conditions such as Residential Direct Sunlight (32.73%) and Highway Fog (28.65%).
Hugging Face Trending Papers
Sep 22
Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation
Standard deployment-ready object detectors for autonomous vehicles degrade in adverse weather and lighting conditions without being trained on extensive domain-specific data. While large-scale vision...
arXiv Computer Vision
Sep 23
PARTE: Plane-Assisted Robust Transformation Estimation for Point Cloud Registration
arXiv:2609.25375v1 Announce Type: cross Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
arXiv Computer Vision
Aug 28
DPA-I2P: Depth-Guided Projective Alignment for Image-to-Point-Cloud Registration in Autonomous Driving
The paper introduces DPA-I2P, a depth-guided projective alignment method for image-to-point-cloud registration in autonomous driving. It employs Ray-Conditioned Metric Depth Encoding and Projection-Consistent Vision Lifting to align depth and visual cues geometrically, and uses Cross-Modal Query Pruning to enhance matching stability. Experiments on KITTI and nuScenes show significant reductions in rotation and translation errors compared to existing implicit baselines.
arXiv Machine Learning
Sep 14
A Multi-Vehicle Dataset with Camera, LiDAR, and Radar Sensors and Scanned 3D Models for Custom Auto-Annotation using RTK-GNSS
The paper introduces a multi-vehicle dataset that includes camera, LiDAR, and radar sensor data along with scanned 3D models of all vehicles. Each vehicle’s pose and continuous kinematics are provided via RTK‑GNSS, enabling precise knowledge of the dynamic surroundings at any time. The dataset supports single‑ and multi‑object recordings with seven target vehicles, allowing evaluation of measurement effects such as occlusion and reflections thanks to known vehicle surface normals.
arXiv Computer Vision
4d ago
Temporal-Aware Fusion for Robust Outdoor LiDAR Localization
The paper introduces TempLoc, a Temporal‑aware Localization framework that improves outdoor LiDAR relocalization by leveraging spatio‑temporal consistency across scans. It first predicts point‑wise global coordinates with uncertainties, then estimates inter‑frame correspondences using an attention‑based Prior Coordinate Generation module, and finally fuses these predictions in an uncertainty‑guided manner to produce a more accurate global 6‑DoF pose. Experiments on the NCLT and Oxford RobotCar datasets show that TempLoc significantly outperforms existing state‑of‑the‑art methods.
arXiv AI
Jun 12
Heterogeneous LiDAR Early Fusion and Learned Re-Ranking Strategy for Robust Long-Term Place Recognition in Unstructured Environments
arXiv:2606. 13503v1 Announce Type: cross Abstract: Robust localization in unstructured environments, such as agricultural fields, is a critical challenge for autonomous systems.