Automatic Image-to-Color Point Cloud Cross-modal Registration Based on Graph Neural Networks and Iterative Reprojection

Image-to-Color point cloud registration establishes a connection between two-dimensional image data and three-dimensional point cloud data, and plays a vital role in the intelligent city, autonomous driving, and robotics field. However, it is still challenging to automatically register an image and its surrounding color point clouds together, due to the special application scenario, unknown camera intrinsic parameters, lack of train data, and a large amount of noise. To overcome those issues, we propose an iterative reprojection architecture that automatically acquires the matched 2D-3D keypoints pairs between the image and the color point clouds by graph optimization method and mapping transfer first, then completes registration by Alternating Direction Method of Multipliers (ADMM). Experiments results show that the proposed method is more accurate than the manual way.