ACPNP: an Efficient Solution for Absolute Camera Pose Estimation from Two Affine Correspondences

In this paper, a novel algorithm to estimate the absolute camera pose is proposed using two affine correspondences (ACs). Exploring the relationship between the affine transformation and the projection equation, six linear constraints are derived, and only two ACs are sufficient to recover the pose. Even though perspective cameras are assumed, the constraints can straightforwardly be generalized to other camera models since they describe the relationship between local affinities and projection. Benefiting from the requirement of less correspondences, the proposed algorithm needs less sampling times when robust estimators like RANSAC are applied, and still performs stably with rather limited number of correspondences. For the improvement of robustness, the affine transformation is further optimized via photometric and epipolar constraints. The proposed method was validated on both synthetic and real-world datasets, which demonstrates that the proposed method yields results superior to the state-of-the-art in terms of accuracy.