LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration
This addresses the need for efficient multi-sensor alignment in autonomous driving, though it is incremental as it extends existing learning-based methods to more modalities.
The paper tackles the problem of target-free calibration for multiple sensor modalities (LiDAR, RGB, and event data) in autonomous systems, achieving competitive performance to bi-modal models and establishing a new baseline for tri-modal calibration.
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.