An Efficient and Accurate Algorithm for the PerspecitvenPoint Problem In this paper, we address the problem of pose estimation from N 2D3D point correspondences, known as the PerspectivenPoint (PnP) problem. Although many solutions have been proposed, it is hard to optimize both computational complexity and accuracy at the same time. In this paper, we propose an accurate and simultaneously efficient solution to the PnP problem. Previous PnP algorithms generally involve two sets of unknowns including the depth of each pixel and the pose of the camera. Our formulation does not involve the depth of each pixel. By introducing some intermediate variables, this formulation leads to a fourth degree polynomial cost function with 3 unknowns that only involves the rotation. In contrast to previous works, we do not address this minimization problem by solving the firstorder optimality conditions using the offtheshelf Grobner basis method, as the Grobner basis method may encounter numeric problems. Instead, we present a method based on linear system null space analysis to provide a robust initial estimation for a Newton iteration. Experimental results demonstrate that our algorithm is comparable to the startoftheart algorithms in terms of accuracy, and the speed of our algorithm is among the fastest algorithms.

Linebased Absolute and Relative Camera Pose Estimation in Structured Environments 3D lines in structured environments encode particular regularity like parallelism and orthogonality. We leverage this structural regularity to estimate the absolute and relative camera poses. We decouple the rotation and translation, and propose a novel rotation estimation method. We decompose the absolute and relative rotations and reformulate the problem as computing the rotation from the Manhattan frame to the camera frame. To compute this rotation, we propose an accurate and efficient twostep method. We first estimate its two degrees of freedom (DOF) by two image lines, and then estimate its third DOF by another image line. For these lines, we assume their associated 3D lines are mutually orthogonal, or two 3D lines are parallel to each other and orthogonal to the third. Thanks to our twostep DOF estimation, our absolute and relative pose estimation methods are accurate and efficient. Moreover, our relative pose estimation method relies on weaker assumptions or less correspondences than existing approaches. We also propose a novel strategy to reject outliers and identify dominant directions of the scene. We integrate it into our pose estimation methods, and show that it is more robust than RANSAC. Experiments on synthetic and realworld datasets demonstrated that our methods outperform stateoftheart approaches.