I2P Registration by Learning the Underlying Alignment Feature Space from Pixel-to-Point Similarities

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Estimating the relative pose between a camera and a LiDAR holds paramount importance in facilitating complex task execution within multi-agent systems. Nonetheless, current methodologies encounter two primary limitations. First, amid the cross-modal feature extraction, they typically employ separate modal branches to extract cross-modal features from images and point clouds. This approach results in the feature spaces of images and point clouds being misaligned, thereby reducing the robustness of establishing correspondences. Second, due to the scale differences between images and point clouds, one-to-many pixel-point correspondences are inevitably encountered, which will mislead the pose optimization. To address these challenges, we propose a framework named I mage-to- P oint cloud registration by learning the underlying alignment feature space from P ixel-to- P oint SIM imilarities (I2P \({}_{\mathbf{ppsim}}\) ) . Central to \(\text{I2P}_{\text{ppsim}}\) is a Shared Feature Alignment Module (SFAM). It is designed under on a coarse-to-fine architecture and uses a weight-sharing network to construct an alignment feature space. Benefiting from SFAM, \(\text{I2P}_{\text{ppsim}}\) can effectively identify the co-view regions between images and point clouds and establish high-reliability 2D-3D correspondences. Moreover, to mitigate the one-to-many correspondence issue, we introduce a similarity maximization strategy termed point-max. This strategy effectively filters out outliers, thereby establishing accurate 2D-3D correspondences. To evaluate the efficacy of our framework, we conduct extensive experiments on KITTI Odometry and Oxford Robotcar. The results corroborate the effectiveness of our framework in improving image-to-point cloud registration. To make our results reproducible, the source codes have been released at https://cslinzhang.github.io/I2P