研究天气导致传感器遮挡对3D检测的影响，发现激光雷达更依赖但易受重雾影响。
Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection
- 在BEVFusion架构下测试摄像头与激光雷达在雾霾等环境下的遮挡影响。
- 摄像头中度遮挡使精度下降41.3%（mAP从35.6%降至20.9%），激光雷达重度遮挡使精度降47.3%。
- 融合模型更依赖激光雷达，其失效导致检测性能大幅下降26.8%。
准确的3D目标检测对自动驾驶车辆在复杂真实环境中安全导航至关重要。鸟瞰图（BEV）表示将多传感器数据投影为俯视空间格式，已成为鲁棒感知的有力方法。尽管基于BEV的融合架构通过多模态集成展现了强大性能，但由雾、霾或物理遮挡等环境条件引起的传感器遮挡对3D检测精度的影响仍研究不足。本文在nuScenes数据集上，使用BEVFusion架构评估了摄像头和激光雷达输出在遮挡下的表现。检测性能采用平均精度（mAP）和nuScenes检测得分（NDS）衡量。结果显示，中度摄像头遮挡导致仅依赖摄像头时mAP下降41.3%（从35.6%降至20.9%）；而激光雷达仅在重度遮挡下性能显著下降，mAP降幅达47.3%（从64.7%降至34.1%），且对远距离检测影响严重。在融合设置中，遮挡摄像头仅造成4.1%的小幅下降（从68.5%降至65.7%），而遮挡激光雷达则导致26.8%的大幅下降（降至50.1%），揭示模型对激光雷达的更强依赖性。结果凸显了未来需发展遮挡感知评估方法及能在部分传感器失效或退化环境下保持精度的融合技术。
原文摘要 · Abstract (English)
Accurate 3D object detection is essential for automated vehicles to navigate safely in complex real-world environments. Bird's Eye View (BEV) representations, which project multi-sensor data into a top-down spatial format, have emerged as a powerful approach for robust perception. Although BEV-based fusion architectures have demonstrated strong performance through multimodal integration, the effects of sensor occlusions, caused by environmental conditions such as fog, haze, or physical obstructions, on 3D detection accuracy remain underexplored. In this work, we investigate the impact of occlusions on both camera and Light Detection and Ranging (LiDAR) outputs using the BEVFusion architecture, evaluated on the nuScenes dataset. Detection performance is measured using mean Average Precision (mAP) and the nuScenes Detection Score (NDS). Our results show that moderate camera occlusions lead to a 41.3% drop in mAP (from 35.6% to 20.9%) when detection is based only on the camera. On the other hand, LiDAR sharply drops in performance only under heavy occlusion, with mAP falling by 47.3% (from 64.7% to 34.1%), with a severe impact on long-range detection. In fused settings, the effect depends on which sensor is occluded: occluding the camera leads to a minor 4.1% drop (from 68.5% to 65.7%), while occluding LiDAR results in a larger 26.8% drop (to 50.1%), revealing the model's stronger reliance on LiDAR for the task of 3D object detection. Our results highlight the need for future research into occlusion-aware evaluation methods and improved sensor fusion techniques that can maintain detection accuracy in the presence of partial sensor failure or degradation due to adverse environmental conditions.
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