LiREC-Net：一种面向激光雷达、RGB 与事件数据的无标定板学习式标定网络
LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration
提出一种无标定板、学习式的三模态联合标定框架，用共享的激光雷达表示（3D 特征 + 投影深度图）统一处理激光雷达-RGB、激光雷达-事件、RGB-事件等模态对，可直接复用于自动驾驶多传感器融合前的在线外参对齐。
原文依据 a target-free, learning-based calibration network that jointly calibrates multiple sensor modality pairs, including LiDAR, RGB, and event data
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论先进的自动驾驶系统依赖多传感器融合来实现更安全、更鲁棒的感知。为使融合有效，直接从自然驾驶场景中进行高精度标定（即无标定板标定）对于精确的多传感器对齐至关重要。现有的基于学习的标定方法通常只针对单一传感器模态对（即双模态设定）设计。与这些方法不同，我们提出 LiREC-Net，一种无标定板、基于学习的标定网络，在统一框架内联合标定包括激光雷达、RGB 与事件数据在内的多个传感器模态对。为减少冗余计算并提升效率，我们引入一种共享的激光雷达表示，同时利用其三维本质特征与投影深度图特征，从而保证跨模态之间更好的一致性。在 KITTI 与 DSEC 等成熟数据集上训练与评估，我们的 LiREC-Net 取得了与双模态模型相当的性能，并为三模态用例树立了新的强基线。
英文原文摘要 1024 字符
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.