无需标定目标，一键校准激光雷达、摄像头与事件相机数据。
LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration
- 统一框架联合校准三类传感器，无需依赖特定标定物。
- 在KITTI和DSEC数据集上性能媲美双模态模型，三模态表现领先。
- 共享激光雷达特征提升效率，兼顾3D结构与深度图信息。
先进自动驾驶系统依赖多传感器融合实现更安全可靠的感知。为实现有效融合，从自然驾驶场景中直接（即无需标定目标）高精度地校准传感器至关重要。现有基于学习的校准方法通常仅针对单一传感器对（即双模态设置）。不同于这些方法，我们提出LiREC-Net，一种无需标定目标的、基于学习的统一网络，可在同一框架内联合校准激光雷达、可见光图像与事件数据等多模态传感器对。为减少冗余计算并提升效率，引入共享的激光雷达表示，同时利用其三维特性与投影深度图特征，确保跨模态的一致性。在KITTI和DSEC等标准数据集上训练与评估，所提方法性能可媲美双模态模型，并为三模态应用场景树立了新的强基准。
原文摘要 · Abstract (English)
Advanced autonomous systems rely on multi-sensor fusion for safer and more robust perception. To enable effective fusion, calibrating directly from natural driving scenes (i.e., target-free) with high accuracy is crucial for precise multi-sensor alignment. Existing learning-based calibration methods are typically designed for only a single pair of sensor modalities (i.e., a bi-modal setup). Unlike these methods, we propose LiREC-Net, a target-free, learning-based calibration network that jointly calibrates multiple sensor modality pairs, including LiDAR, RGB, and event data, within a unified framework. To reduce redundant computation and improve efficiency, we introduce a shared LiDAR representation that leverages features from both its 3D nature and projected depth map, ensuring better consistency across modalities. Trained and evaluated on established datasets, such as KITTI and DSEC, our LiREC-Net achieves competitive performance to bi-modal models and sets a new strong baseline for the tri-modal use case.
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