With the rapid development of digital cities and machine vision, 3D reconstruction techniques for outdoor scenes based on 3D point cloud data are becoming more and more important in autonomous driving, robot path analysis, and intelligent navigation. However, the complexity of outdoor scenes and the uncertainties in data acquisition (environmental noise, sparse and uneven distribution of point clouds, low overlap rate, etc.) pose great challenges to feature extraction and reconstruction accuracy. To address these issues, this study proposes a multi-scale coherence-based point cloud registration network (MSC-Net). The network utilizes geometric coding to add geometric constraints for point cloud matching, and further improves the matching accuracy through multi-scale feature extraction and consistency constraint strategies. Experimental results on the KITTI dataset show that MSC-Net performs well in terms of registration accuracy, achieving the relative translation error of 5.4 cm and the relative rotation error of 0.22°, both of which are improved compared to existing algorithms. In addition, the proposed method is evaluated on the 3DMatch and 3DLoMatch datasets, achieving excellent results in all metrics, with the highest registration recall of 94.5% on the 3DMatch dataset.