轻量模块提升自动驾驶多传感器融合的稳定性。
Post Fusion Bird's Eye View Feature Stabilization for Robust Multimodal 3D Detection
- 后融合阶段优化鸟瞰图特征，不改原模型架构。
- 相机失效时检测精度提升1.2%，低光下提升4.4% mAP。
- 适合部署在已有系统上，对性能影响小且抗干扰强。
摄像头与激光雷达融合广泛用于自动驾驶中的3D物体检测。然而，现有鸟瞰图（BEV）融合检测器在域偏移和传感器故障下性能显著下降，限制了真实场景下的可靠性。现有鲁棒性方法通常需要修改融合架构或重新训练专用模型，难以集成到已部署系统中。本文提出后融合稳定器（PFS），一个轻量级模块，作用于现有检测器的中间BEV表示，生成优化后的特征图供原始检测头使用。该设计在域偏移下稳定特征统计分布，抑制传感器退化影响的空间区域，并通过残差修正自适应恢复弱化线索。作为近恒等变换，PFS在保持性能的同时提升了多种异常条件下的鲁棒性。在nuScenes基准测试中，PFS在多个故障模式下达到领先效果，尤其在相机完全失效时提升1.2% mAP，低光照条件下提升4.4% mAP，参数量仅3.3 M。
原文摘要 · Abstract (English)
Camera-LiDAR fusion is widely used in autonomous driving to enable accurate 3D object detection. However, bird's-eye view (BEV) fusion detectors can degrade significantly under domain shift and sensor failures, limiting reliability in real-world deployment. Existing robustness approaches often require modifying the fusion architecture or retraining specialized models, making them difficult to integrate into already deployed systems. We propose a Post Fusion Stabilizer (PFS), a lightweight module that operates on intermediate BEV representations of existing detectors and produces a refined feature map for the original detection head. The design stabilizes feature statistics under domain shift, suppresses spatial regions affected by sensor degradation, and adaptively restores weakened cues through residual correction. Designed as a near-identity transformation, PFS preserves performance while improving robustness under diverse camera and LiDAR corruptions. Evaluations on the nuScenes benchmark demonstrate that PFS achieves state-of-the-art results in several failure modes, notably improving camera dropout robustness by +1.2% and low-light performance by +4.4% mAP while maintaining a lightweight footprint of only 3.3 M parameters.
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