ZIL: Zero-shot Image-to-LiDAR Registration
Image-to-LiDAR registration estimates the camera pose of an image with respect to a LiDAR point cloud. It has diverse applications in autonomous driving, robot navigation etc. However, state-of-the-art (SOTA) methods still 1) mostly assume same-frame inputs, struggling with the image and point cloud from distant frames; 2) rely on domain-specific training, failing to generalize to unseen scenarios. We propose ZIL, the first foundation model for zero-shot non-synchronized image-to-LiDAR registration. ZIL encodes the input image and point cloud with the Vision and Point Transformers. In addition to regressing the relative pose, ZIL also learns to predict 3D coordinates, which substantially ...
| Authors | Zijun Li, Xiaotian Sun, Xuelun Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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