SynCalib: Breaking Dataset Shortcuts in LiDAR-Camera Calibration via Extrinsic-Controllable Synthesis
To address this limitation, we propose a highly generalizable data generation framework tailored for training LiDAR-camera calibration networks.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]SynCalib: Breaking Dataset Shortcuts in LiDAR-Camera Calibration via Extrinsic-Controllable SynthesisarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 02:59 AM
To address this limitation, we propose a highly generalizable data generation framework tailored for training LiDAR-camera calibration networks.
Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics.
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