3DR-Reg: Rethinking Color Point Cloud Registration as 3D Reconstruction

Color point cloud registration still largely follows the feature-matching paradigm, in which point cloud features are matched before estimating a rigid transformation. Recent 3D reconstruction foundation models offer a different way to organize cross-view information by directly inferring structured scene geometry. We introduce 3DR-Reg, which reformulates color point cloud registration as 3D reconstruction. Rather than matching independently learned descriptors, 3DR-Reg reconstructs a shared 3D scene from the color information of two input point clouds using a pretrained 3D reconstruction foundation model. These predictions enable registration through camera poses, cross-view correspondences, or a shared reference frame. To improve correspondence-based registration, we further introduce a geometry-guided correspondence refinement module that resolves ambiguous correspondences through geometric consistency and global context. Experiments on 3DMatch and 3DLoMatch demonstrate state-of-the-art registration performance, with mean rotation/translation errors of $0.69^{\circ }$/2.14 cm and $3.77^{\circ }$/13.15 cm, respectively. Across multiple frozen 3D reconstruction models, reconstruction predictions support accurate registration without learning registration-specific representations. For the first time, we demonstrate that a 3D reconstruction foundation model can directly serve as the basis for color point cloud registration.