Globally Optimal Camera Orientation Estimation from Line Correspondences by BnB algorithm

Globally Optimal Pose Estimation from Line-Plane Correspondences r930

3D Camera Pose Estimation Using Line Correspondences and 1D Homographies

This paper describes a new method for matching line segments between two images in order to compute the relative camera pose. This approach improves the camera pose for images lacking stable point features but where straight line segments are available. The line matching algorithm is divided into two stages: At first, scale-invariant feature points along the lines are matched incorporating a on...

Camera Calibration Using Line Correspondences

In this paper, a method of determining the essential matrix for uncalibrated cameras is given, based on line matches in three images. The three cameras may have different unknown calibrations, and the essential matrices corresponding to each of the three pairs of cameras may be determined. Determination of the essential matrix for uncalibrated cameras is important, forming the basis for many al...

Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm.

PURPOSE This article focuses on developing a novel noniterative fat water decomposition algorithm more robust to fat water swaps and related ambiguities. METHODS Field map estimation is reformulated as a constrained surface estimation problem to exploit the spatial smoothness of the field, thus minimizing the ambiguities in the recovery. Specifically, the differences in the field map-induced ...

Absolute 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 ‘DLT-Combined-Lines’ based on the Direct Linear Transformation (DLT) algorithm, which benefits from a new combination of two exist...