Online LiDAR-camera calibration is a persistent challenge in robotics. Data-driven deep-learning approaches are attractive because they deliver high precision without requiring calibration targets. But these models can quietly rely on incidental correlations in their training data — "dataset shortcuts" — rather than on geometric reasoning that generalizes.
SynCalib addresses this by synthesizing training examples with explicitly controllable extrinsics. By varying the relative pose between sensor views during synthesis, the method forces the model to learn from the actual spatial relationship rather than from static cues embedded in a particular dataset. This reduces shortcut dependence and strengthens the model's ability to handle unseen real-world configurations.
The paper reports improved cross-dataset performance in real LiDAR-camera calibration tasks. Because this summary draws on a single source, there are no conflicting findings to compare. The contribution is best understood as an augmentation strategy that targets a known failure mode of learned calibration models rather than a fundamentally new network architecture.