SCAP: Semantic Prototype Alignment for Robust Point Cloud Registration

Point cloud rigid registration is a fundamental problem in robotics, 3D reconstruction, and augmented reality. However, existing methods predominantly rely on local geometric neighborhoods, which fail to capture higher-order semantic structures and thus degrade performance under noisy or complex geometry conditions. To address these limitations, we propose SCAP, a new point cloud registration paradigm that transforms feature interaction from geometry-driven to semantics–geometric co-driven. Specifically, a semantic prototype extractor is devised to abstract high-level semantic prototypes through graph embedding and clustering, thereby mitigating sensitivity to local feature noise. Since semantic abstraction alone cannot guarantee consistent correspondences across point clouds, SCAP performs a prototype alignment path learning to infer reliable semantic mappings through optimal transport. To enhance cross-layer feature integration and prevent redundant attention, an alignment-driven cross-layer transformer is proposed to incorporate the learned priors into the attention mechanism, thereby enabling feature aggregation with improved semantic coherence and local precision. Extensive experiments on ModelNet, ModelLoNet, 3DMatch, and 3DLoMatch demonstrate that our SCAP consistently surpasses state-of-the-art approaches, showing superior robustness and generalization in challenging scenarios with noise and partial overlap. The code will be available at https://github.com/Zhou-111jy/SCAP.git.