Abstract 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 “DLT-Combined-Lines” 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 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 the accuracy of the proposed method. For large line sets (10 and more), the method is comparable to the state-of-the-art in accuracy of orientation estimation. It achieves state-of-the-art accuracy in estimation of camera position and it yields the smallest reprojection error under strong image noise. The method achieves top-3 results on real world data. The proposed method is also highly computationally effective, estimating the pose of 1000 lines in 12 ms on a desktop computer.
Recently, @cite_10 developed a DLT method, which acts on 3D lines directly. The lines are parameterized using Plücker coordinates, hence the name of the method is . The method yields more accurate estimates of camera orientation than DLT-Lines at the cost of a bit larger reprojection error and slightly lower computational efficiency. Also, the minimum number of lines required is 9.
@cite_10
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In this paper we approach the problem of in- situ camera calibration using an auxiliary mobile color-depth camera. The calibration is based on image lines and the Direct Linear Transformation (DLT). Calibration includes intrinsic and extrinsic parameters, and radial distortion. Traditionally, camera calibration methods based on DLT combine 2D and 3D points. Using 2D image lines allows adding simple image processing methods for fine tuning the calibration data (lines). Experimental comparison of points and lines based calibration shows that the automatic fine tuning of the lines is beneficial for the calibration process in noisy conditions. Experiments on both synthetic and real setups show that despite the low computational complexity of the DLT, the proposed calibration methodology yields promising accurate results. I. I NTRODUCTION