Abstract: Point cloud registration is still a challenging and open problem. For example, when the overlap between two point clouds is extremely low, geo-only features may be not sufficient. Therefore, it is important to further explore how to utilize color data in this task. Under such circumstances, we propose ColorPCR for color point cloud registration with multi-stage geometric-color fusion. We design a Hierarchical Color Enhanced Feature Extraction module to extract multi-level geometric-color features, and a GeoColor Superpoint Matching Module to encode transformation-invariant geo-color global context for robust patch correspondences. In this way, both geometric and color data can be used, thus leading to robust performance even under extremely challenging scenarios, such as low overlap between two point clouds. To evaluate the performance of our method, we colorize 3DMatch/3DLoMatch datasets as Color3DMatch/Color3DLoMatch and evaluations on these datasets demonstrate the effectiveness of our proposed method. Our method achieves state-of-the-art registration recall of $\textbf{97.5}$%/$\textbf{88.9}$% on them.