Pose Estimation from Line Correspondences using Direct Linear Transformation.

This work is concerned with camera pose estimation from correspondences of 3D/2D lines, i.e. with the Perspective-n-Line (PnL) problem. We focus on large line sets, which can be efficiently solved by methods using linear formulation of PnL. We propose a novel method u0027DLT-Combined-Linesu0027 based on the Direct Linear Transformation (DLT) algorithm, which benefits from a new combination of two existing DLT methods for pose estimation. The method represents 2D structure by lines, and 3D structure by both points and lines. The redundant 3D information reduces the minimum of required line correspondences to 5. A cornerstone of the method is a combined projection matrix estimated by the DLT algorithm. It contains multiple estimates of camera rotation and translation, which can be recovered after enforcing constraints of the matrix. Multiplicity of the estimates is exploited to improve accuracy of the proposed method. For large line sets (10 and more), the method achieves state-of-the-art accuracy on synthetic data even under strong image noise. Moreover, it is the most accurate method on real world data, outperforming the state-of-the-art by a large margin. The proposed method is also highly computationally effective, estimating the pose of 1000 lines in 12 ms on a desktop computer.