SynCalib: Breaking Dataset Shortcuts in LiDAR-Camera Calibration via Extrinsic-Controllable Synthesis
Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approaches have emerged, offering high-precision, targetless calibration capabilities. However, existing methods often suffer from a critical dataset shortcut phenomenon due to the lack of extrinsic diversity in their training data. Networks tend to overfit to fixed nominal extrinsic configurations, leading to severe performance degradation and poor generalization in real-world deployments. To address this limitation, we propose a highly generalizable data generation framework tailored for training LiDAR-camera calibration networks. Our framework l...
| Authors | Huihua Zhu, Shuyang Zhang, Dongqing Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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