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 leverages existing omnidirectional image datasets annotated with depth ground truth to synthesize multi-modal calibration data under rigorous extrinsic constraints. Extensive experiments demonstrate that the data generated by our pipeline significantly enhances the generalization and robustness of typical learning-based calibration networks. Furthermore, we conduct comprehensive real-world evaluations across various camera-LiDAR configurations, validating the zero-shot transfer capability of models trained exclusively on our synthetic data. To facilitate future research and community use, the code for data generation, the pre-trained models, and online benchmark datasets will be made publicly available.

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