The paper introduces SR‑BEVS, a sparse recovery learning technique that enhances both clean accuracy and robustness to real‑world corruptions for camera‑based bird’s‑eye view (BEV) 3D object detection models. By pruning weights based on magnitude, generating diverse sparse models, retraining them with varied learning rates, and averaging the recovered models into a single dense model, the method achieves up to 33.8% improvement in corruption robustness and a 1.8% gain in clean accuracy over the Fast‑BEV baseline on the nuScenes dataset. The approach adds no inference overhead and preserves a connected low‑loss basin, suggesting applicability to other BEV models.
arXiv:2609.23541v1 Announce Type: new Abstract: Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and...
The paper introduces Post Fusion Stabilizer (PFS), a lightweight module that refines intermediate bird’s‑eye view (BEV) feature maps in existing camera‑LiDAR fusion detectors. PFS stabilizes feature statistics under domain shift, suppresses regions affected by sensor degradation, and adaptively restores weakened cues via residual correction, acting as a near‑identity transformation. On the nuScenes benchmark, PFS achieves state‑of‑the‑art robustness, notably improving camera dropout robustness by +1.2% and low‑light performance by +4.4% mAP while adding only 3.3 M parameters.
arXiv:2606. 24353v1 Announce Type: cross Abstract: Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving.
The paper presents a method for creating LiDAR-based 3D object detectors that remain accurate under point‑cloud corruptions while meeting strict latency and memory constraints. It achieves robustness by generating corrupted samples that mimic extreme weather and training detectors to focus on hard samples, guided by perturbation sensitivity estimated from an ensemble of seven heterogeneous detectors. Efficiency is improved by narrowing the network and using offline neighborhood‑density measurements to reduce the number of points processed at each sampling stage, resulting in significant gains in AP under corruption and reductions in inference latency and GPU memory usage across multiple datasets and detector architectures.
arXiv:2607.29237v2 Announce Type: replace Abstract: LiDAR scene flow is the task of estimating point-wise motion from sequential point clouds. It is essential for autonomous driving, as it provides c...
The paper addresses performance discrepancy in cross-domain 3D class‑incremental learning, where 3D point clouds from heterogeneous sources cause varying degrees of performance loss beyond catastrophic forgetting. The authors introduce the Domain3D‑CIL protocol and adapt existing CIL methods to 3D, showing consistent discrepancy across baselines. They propose PolyMem, an exemplar‑free approach that models high‑order feature statistics to improve cross‑domain robustness and reduce performance discrepancy.
arXiv:2610.08573v1 Announce Type: new Abstract: Cooperative perception improves autonomous driving by sharing complementary observations among vehicles and roadside infrastructure for 3D object detec...
arXiv:2609.14185v1 Announce Type: cross Abstract: Bird's Eye View (BEV)-based multi-view 3D object detection suffers from challenges of computational complexity, multi-scale feature extraction, and e...
SARFusion introduces a scene-aware routing approach for camera‑LiDAR 3D object detection, decoupling object‑query decoding into separate camera, LiDAR, and fusion branches. By estimating a global scene reliability prior and incorporating object‑level evidence, each query is routed to the most suitable branch, reducing cross‑modal interference. The method achieves strong performance on the nuScenes test set (72.5 mAP, 74.4 NDS) and demonstrates robustness to sensor corruptions and environmental changes.
LiDAR-based 3D object detection is a core perception service for autonomous driving, but deployment requires robustness to point-cloud corruption while meeting latency and memory requirements. Existin...
arXiv:2606. 02603v1 Announce Type: cross Abstract: Camouflaged object detection has improved substantially, but most standard benchmarks evaluate models only on clean images.
The paper evaluates five vision‑language models on autonomous driving tasks under various visual input conditions, finding that visual corruption affects accuracy and confidence differently across models and datasets. It then tests Visual Evidence Augmentation (VEA) as an inference‑time technique to enhance reliability, observing mixed improvements depending on the model and setting.