Hugging Face Trending Papers
Understanding and Repairing Cooperative Perception under Adverse Weather
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arXiv Computer Vision
Oct 27
Understanding and Repairing Cooperative Perception under Adverse Weather
arXiv:2610.35110v1 Announce Type: new Abstract: A cooperative 3D detector can miss a vehicle even when the LiDAR points on it remain unchanged. We study this failure under adverse weather as Weather-...
arXiv Computer Vision
Oct 12
Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection
The paper investigates how snow affects LiDAR-based object detection, finding that detection accuracy actually improves with higher snow intensity. This counter‑intuitive result is explained by an increase in object point count due to snow accumulation, which outweighs the noise introduced by falling snow. The authors argue that broad weather severity bins can mislead robustness assessments unless atmospheric noise and surface accumulation effects are disentangled.
arXiv AI
Jul 28
ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness
arXiv:2607. 23537v1 Announce Type: new Abstract: Autonomous driving under adverse weather remains a critical challenge, yet existing vision-language benchmarks mainly evaluate under standard conditions, synthetic corruptions, or single modality.
arXiv Computer Vision
1d ago
Can BEV Perception Gracefully Degrade under Sensor Failures?
The paper introduces Grace-BEV, a lightweight framework that enhances the robustness of Lift‑Splat‑Shoot (LSS) based bird’s‑eye view (BEV) perception models against sensor corruptions. Grace‑BEV operates in aligned BEV space, combining a LiDAR‑guided image‑BEV expert with a LiDAR‑independent visual expert, and uses a TrustGate Router and FailSafe Fusion Block to mix and recalibrate their outputs. Experiments on nuScenes-R and nuScenes-C show that Grace‑BEV maintains strong performance under diverse corruptions, recovers up to 34.7 mAP when LiDAR is lost, and even improves clean accuracy by up to 1.4 mAP with only 1.2M additional parameters.
Hugging Face Trending Papers
Jun 8
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
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%).