ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistency
This work addresses a key limitation in point cloud registration for applications like autonomous driving, offering a plug-and-play solution with significant performance gains, though it is incremental in leveraging semantic information.
The paper tackles point cloud registration challenges in low-overlap scenarios by proposing ML-SemReg, a framework that uses multi-level semantic consistency to improve matching, resulting in a 34 percentage point increase in Registration Recall on the KITTI dataset.
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}