Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration

This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation. We use constraints provided by orthogonal scene planes to recover the focal length. We show that solvers using feature combinations can recover more accurate calibrations than solvers using only one feature type on scenes that have a balance of lines and texture. We also show that the proposed solvers are complementary and can be used together in a RANSAC-based estimator to improve auto-calibration accuracy. State-of-the-art performance is demonstrated on a standard dataset of lens-distorted urban images. The code is available at https://github.com/ylochman/single-view-autocalib

Analysis of camera pose estimation using 2D scene features for augmented reality applications

La réalité augmentée (RA) a récemment eu un impact énorme sur les ingénieurs civils et les travailleurs de l'industrie de la construction, ainsi que sur leur interaction avec les plans ar-chitecturaux. La RA introduit une superposition du modèle 3D d'un bâtiment sur une image 2D non seulement comme une image globale, mais aussi potentiellement comme une repré-sentation complexe de ce qui va être construit et qui peut être visualisée par l'utilisateur. Pour insérer un modèle 3D, la caméra doit être localisée par rapport à son environnement. La lo-calisation de la caméra consiste à trouver les paramètres extérieurs de la caméra (i.e. sa po-sition et son orientation) par rapport à la scène observée et ses caractéristiques. Dans ce mémoire, des méthodes d'estimation de la pose de la caméra (position et orientation) par rapport à la scène utilisant des correspondances cercle-ellipse et lignes droites-lignes droites sont explorées. Les cercles et les lignes sont deux des caractéristiques géométriques qui sont principalement présentes dans les structures et les bâtiments. En fonction de la rela-tion entre les caractéristiques 3D et leurs images 2D correspondantes détectées dans l'image, la position et l'orientation de la caméra sont estimées.Augmented reality (AR) had recently made a huge impact on field engineers and workers in construction industry, as well as the way they interact with architectural plans. AR brings in a superimposition of the 3D model of a building onto the 2D image not only as the big picture, but also as an intricate representation of what is going to be built. In order to insert a 3D model, the camera has to be localized regarding its surroundings. Camera localization con-sists of finding the exterior parameters (i.e. its position and orientation) of the camera with respect to the viewed scene and its characteristics. In this thesis, camera pose estimation methods using circle-ellipse and straight line corre-spondences has been investigated. Circles and lines are two of the geometrical features that are mostly present in structures and buildings. Based on the relationship between the 3D features and their corresponding 2D data detected in the image, the position and orientation of the camera is estimated

Robust Estimation of Trifocal Tensors Using Natural Features for Augmented Reality Systems

Augmented reality deals with the problem of dynamically augmenting or enhancing the real world with computer generated virtual scenes. Registration is one of the most pivotal problems currently limiting AR applications. In this paper, a novel registration method using natural features based on online estimation of trifocal tensors is proposed. This method consists of two stages: offline initialization and online registration. Initialization involves specifying four points in two reference images respectively to build the world coordinate system on which a virtual object will be augmented. In online registration, the natural feature correspondences detected from the reference views are tracked in the current frame to build the feature triples. Then these triples are used to estimate the corresponding trifocal tensors in the image sequence by which the four specified points are transferred to compute the registration matrix for augmentation. The estimated registration matrix will be used as an initial estimate for a nonlinear optimization method that minimizes the actual residual errors based on the Levenberg-Marquardt (LM) minimization method, thus making the results more robust and stable. This paper also proposes a robust method for estimating the trifocal tensors, where a modified RANSAC algorithm is used to remove outliers. Compared with standard RANSAC, our method can significantly reduce computation complexity, while overcoming the disturbance of mismatches. Some experiments have been carried out to demonstrate the validity of the proposed approach

An Efficient Solution to the Homography-Based Relative Pose Problem With a Common Reference Direction

International audienceIn this paper, we propose a novel approach to two-view minimal-case relative pose problems based on homography with a common reference direction. We explore the rank-1 constraint on the difference between the Euclidean homog-raphy matrix and the corresponding rotation, and propose an efficient two-step solution for solving both the calibrated and partially calibrated (unknown focal length) problems. We derive new 3.5-point, 3.5-point, 4-point solvers for two cameras such that the two focal lengths are unknown but equal, one of them is unknown, and both are unknown and possibly different, respectively. We present detailed analyses and comparisons with existing 6-and 7-point solvers, including results with smart phone images

Complete initial solutions for iterative pose estimation from planar objects

Camera pose estimation from the image of a planar object has important applications in photogrammetry and computer vision. In this paper, an efficient approach to find the initial solutions for iterative camera pose estimation using coplanar points is proposed. Starting with homography, the proposed approach provides a least-squares solution for absolute orientation, which has a relatively high accuracy and can be easily refined into one optimal pose that locates local minima of the according error function by using Gauss-Newton scheme or Lu's orthogonal iteration algorithm. In response to ambiguities that exist in pose estimation from planar objects, we propose a novel method to find initial approximation of the second pose, which is different from existing methods in its concise form and clear geometric interpretation. Thorough testing on synthetic data shows that combined with currently employed iterative optimization algorithm, the two initial solutions proposed in this paper can achieve the same accuracy and robustness as the best state-of-the-art pose estimation algorithms, while with a significant decrease in computational cost. Real experiment is also employed to demonstrate its performance