Relative pose measurement for noncooperative objects is an important part of 3D shape recognition and motion tracking. The methods based on scanning point clouds have better environmental adaptability and stability than image-based methods. However, the discrete points obtained from a continuous surface are sparse, which leads to point-to-point dislocations in the overlapping area and seriously reduces the accuracy. Therefore, this paper proposed a relative-pose-measurement algorithm based on double-constrained intersurface mutual projections. First, the initial corresponding set was constructed using mutual projections between the areas with similar feature descriptors, and then the final corresponding set was determined through the rigid-transformation-consistency constraint to improve the accuracy of the matchings and achieve a high-accuracy relative pose measurement. In the Stanford dataset, the rotation error and translation error were reduced by 19.3% and 13.4%, respectively. Furthermore, based on the proposed evaluation method, which separated the error of the pose-measurement algorithm from that of the instrument, the experiments were carried out with a self-made swept-frequency interferometer. The rotation error was reduced by 39.8%, and the surface deviation was reduced by 4.9%, which further proved the advancement of the method.