CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration

• URL: http://arxiv.org/abs/2309.14660v5
• Date: Thu, 12 Sep 2024 09:43:01 GMT
• Title: CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration
• Authors: Shuhao Kang, Youqi Liao, Jianping Li, Fuxun Liang, Yuhao Li, Xianghong Zou, Fangning Li, Xieyuanli Chen, Zhen Dong, Bisheng Yang,
• Abstract summary: CoFiI2P is a novel I2P registration network that extracts correspondences in a coarse-to-fine manner. In the coarse matching phase, a novel I2P transformer module is employed to capture both homogeneous and heterogeneous global information. In the fine matching module, point/pixel pairs are established with the guidance of super-point/super-pixel correspondences.
• Score: 9.57539651520755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 paper 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 transform 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.Additional experiments on the Nuscenes datasets confirm our method's generalizability. The project page is available at \url{https://whu-usi3dv.github.io/CoFiI2P}.

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