不依赖投影，直接在原空间对齐图像与点云，提升大偏差下的标定精度。
Native-Domain Cross-Attention for Camera-LiDAR Extrinsic Calibration Under Large Initial Perturbations
- 在原始图像块与点云组间直接建立跨模态注意力，避免投影失真。
- 在KITTI上88%、nuScenes上99%的案例中实现精准标定，显著优于现有方法。
- 适合初始误差大的真实场景，尤其适用于自动驾驶多传感器融合系统。
精准的相机-LiDAR融合依赖于精确的外参标定，其核心在于在存在较大初始偏差时建立可靠的跨模态对应关系。现有基于学习的方法通常将激光点投影到深度图进行特征融合，但在外参初始化偏离真值较大时会扭曲三维几何结构，导致性能下降。为此，我们提出一种外参感知的跨注意力框架，直接在图像块与激光点组的原生域中对齐。该注意力机制显式地将外参假设注入对应建模过程，无需依赖投影的二维深度图，实现了几何一致的跨模态交互。在KITTI和nuScenes基准上的大量实验表明，本方法在准确性和鲁棒性上均优于现有最优方法。在大外参扰动下，我们的方法在KITTI上88%的案例中实现准确标定，在nuScenes上达到99%，远超第二优基线。代码已开源至https://github.com/gitouni/ProjFusion，供社区使用。
原文摘要 · Abstract (English)
Accurate camera-LiDAR fusion relies on precise extrinsic calibration, which fundamentally depends on establishing reliable cross-modal correspondences under potentially large misalignments. Existing learning-based methods typically project LiDAR points into depth maps for feature fusion, which distorts 3D geometry and degrades performance when the extrinsic initialization is far from the ground truth. To address this issue, we propose an extrinsic-aware cross-attention framework that directly aligns image patches and LiDAR point groups in their native domains. The proposed attention mechanism explicitly injects extrinsic parameter hypotheses into the correspondence modeling process, enabling geometry-consistent cross-modal interaction without relying on projected 2D depth maps. Extensive experiments on the KITTI and nuScenes benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches in both accuracy and robustness. Under large extrinsic perturbations, our approach achieves accurate calibration in 88% of KITTI cases and 99% of nuScenes cases, substantially surpassing the second-best baseline. We have open sourced our code on https://github.com/gitouni/ProjFusion to benefit the community.
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