  Recent advances in point cloud registration mostly leverage geometric
information. Although these methods have yielded promising results, they still
struggle with problems of low overlap, thus limiting their practical usage. In
this paper, we propose ML-SemReg, a plug-and-play point cloud registration
framework that fully exploits semantic information. Our key insight is that
mismatches can be categorized into two types, i.e., inter- and intra-class,
after rendering semantic clues, and can be well addressed by utilizing
multi-level semantic consistency. We first propose a Group Matching module to
address inter-class mismatching, outputting multiple matching groups that
inherently satisfy Local Semantic Consistency. For each group, a Mask Matching
module based on Scene Semantic Consistency is then introduced to suppress
intra-class mismatching. Benefit from those two modules, ML-SemReg generates
correspondences with a high inlier ratio. Extensive experiments demonstrate
excellent performance and robustness of ML-SemReg, e.g., in hard-cases of the
KITTI dataset, the Registration Recall of MAC increases by almost 34 percentage
points when our ML-SemReg is equipped. Code is available at
\url{https://github.com/Laka-3DV/ML-SemReg}
