Evaluating 3D-2D correspondences for accurate camera pose estimation from a single image

Due to occlusion, appearance and disappearance of points, and noise distribution in image data, no matter what algorithms are used to register a 3D model and its projective image, they are always likely to introduce false matches. In this paper, we propose a novel algorithm to evaluate the established correspondences for more accurate camera pose estimation. The novel algorithm is based on two novel rigid motion constraints that represent necessary conditions for any pair of a 3D and a 2D point to represent a real 3D-2D correspondence. We then prove that as long as 3D-2D point matches satisfy the two novel rigid motion constraints, they are theoretically guaranteed to represent real correspondences. A large number of experiments based on both computer simulation and real images have indeed demonstrated that the proposed algorithm is accurate and robust for the evaluation of established correspondences and thus, leading to more accurate and robust camera pose estimation.