NCGR：面向BEV 3D目标检测中相机外参扰动的噪声条件门控校正
NCGR: Noise-Conditional Gated Rectification for Camera Extrinsic Perturbations in BEV 3D Object Detection
AI总结：
针对BEV 3D检测中相机外参扰动导致的性能下降，本文提出NCGR方法，通过预测2D校正偏移补偿投影误差，在nuScenes数据集上的五相机动态压力测试中表现优于BEVFormer和CAPE。
AI中文摘要：
基于相机的鸟瞰图（BEV）3D检测通常假设相机外参准确且固定。在使用空间交叉注意力（SCA）的检测器中，外参扰动会使BEV参考点的图像平面投影发生位移，导致查询从错误区域采样特征，从而降低检测性能。为解决该问题，本文提出噪声条件门控校正（NCGR），用于补偿投影误差而无需显式估计完整的六自由度外参校正。对于每个查询-相机对，预测一个2D校正偏移，并由相机级门控进行调制，以在原生可变形采样前校正基础投影。训练期间，用于构建条件和门控的、由扰动生成的量会通过调度插值，逐渐替换为由相机特征预测的辅助标量生成的对应量。这种过渡使模型能够在无扰动元数据的情况下进行推理。训练期间，采用权重共享的干净教师/扰动学生对，校正模块由两个分支间的BEV一致性目标进行监督。NCGR在nuScenes数据集上进行了模拟动态和静态外参扰动的评估。在五相机动态压力测试中，NCGR达到39.69%的NDS，而BEVFormer为28.00%，CAPE为33.23%；在干净外参下，NCGR保持与BEVFormer相当的性能。
英文摘要：
Camera-based bird's-eye-view (BEV) 3D detection typically assumes accurate and fixed camera extrinsics. In detectors using spatial cross-attention (SCA), extrinsic perturbations displace the image-plane projections of BEV reference points, causing queries to sample features from incorrect regions and degrading detection performance. To address this failure mode, Noise-Conditional Gated Rectification (NCGR) is proposed to compensate for projection errors without explicitly estimating a full six-degree-of-freedom extrinsic correction. For each query-camera pair, a 2D rectification offset is predicted and modulated by a camera-level gate to rectify the base projection before native deformable sampling. During training, the perturbation-derived quantities used to construct the condition and gate are gradually replaced through scheduled interpolation by counterparts generated from an auxiliary scalar predicted from camera features. This transition enables blind inference without perturbation metadata. During training, a weight-shared clean-teacher/perturbed-student pair is used, and the rectification module is supervised by a BEV-consistency objective between the two branches. NCGR is evaluated on nuScenes with simulated dynamic and static extrinsic perturbations. In a five-camera dynamic stress test, NCGR achieves 39.69% NDS, compared with 28.00% for BEVFormer and 33.23% for CAPE. Under clean extrinsics, NCGR maintains performance comparable to that of BEVFormer.
