Image-to-point cloud (I2P) registration is a fundamental task for robots and autonomous vehicles to achieve cross-modality data fusion and localization. Current I2P registration methods primarily focus on estimating correspondences at the point or pixel level, often neglecting global alignment. As a result, I2P matching can easily converge to a local optimum if it lacks high-level guidance from global constraints. To improve the success rate and general robustness, this letter introduces CoFiI2P, a novel I2P registration network that extracts correspondences in a coarse-to-fine manner. First, the image and point cloud data are processed through a two-stream encoder-decoder network for hierarchical feature extraction. Second, a coarse-to-fine matching module is designed to leverage these features and establish robust feature correspondences. Specifically, in the coarse matching phase, a novel I2P transformer module is employed to capture both homogeneous and heterogeneous global information from the image and point cloud data. This enables the estimation of coarse super-point/super-pixel matching pairs with discriminative descriptors. In the fine matching module, point/pixel pairs are established with the guidance of super-point/super-pixel correspondences. Finally, based on matching pairs, the transformation matrix is estimated with the EPnP-RANSAC algorithm. Experiments conducted on the KITTI Odometry dataset demonstrate that CoFiI2P achieves impressive results, with a relative rotation error (RRE) of 1.14 degrees and a relative translation error (RTE) of 0.29 meters, while maintaining real-time speed. These results represent a significant improvement of 84% in RRE and 89% in RTE compared to the current state-of-the-art (SOTA) method. Additional experiments on the Nuscenes dataset confirm our method's generalizability.

Cross-source (CS) point cloud registration is a prerequisite for effectively leveraging the complementary information of multiple 3-D sensors. However, existing point cloud registration methods have primarily focused on the registration of mono-source point clouds and typically fail to register CS data with varying noise patterns and capture characteristics. In this article, we present a new algorithm for CS point cloud registration between mobile laser scanning (MLS) point clouds and stereo-reconstructed point clouds (SPCs). Our method has two key designs. First, we design a novel descriptor with in-plane rotation equivariance by leveraging the accessible gravity prior, yielding strong descriptiveness, better robustness, and improved efficiency. Second, based on the noise pattern of SPCs, a novel disparity-weighted correspondence scoring strategy is proposed to strengthen the registration accuracy. In comparison to existing registration baselines, our method achieves a 32.6% higher registration recall (RR) on CS datasets of KITTI and KITTI-360 and a 23.1% higher RR on mono-source datasets of KITTI. Notably, our method also outperforms RANdom SAmple Consensus (RANSAC)-based methods in terms of computational efficiency with a 10 $\times \,\,\sim $ 70 $\times $ speedup. The source code and datasets have been available at https://github.com/WHU-USI3DV/MSReg .