0000000000827304
AUTHOR
Adlane Habed
A New Set of Quartic Trivariate Polynomial Equations for Stratified Camera Self-calibration under Zero-Skew and Constant Parameters Assumptions
This paper deals with the problem of self-calibrating a moving camera with constant parameters. We propose a new set of quartic trivariate polynomial equations in the unknown coordinates of the plane at infinity derived under the no-skew assumption. Our new equations allow to further enforce the constancy of the principal point across all images while retrieving the plane at infinity. Six such polynomials, four of which are independent, are obtained for each triplet of images. The proposed equations can be solved along with the so-called modulus constraints and allow to improve the performance of existing methods.
Self-calibration of a PTZ Camera Using New LMI Constraints
In this paper, we propose a very reliable and flexible method for self-calibrating rotating and zooming cameras - generally referred to as PTZ (Pan-Tilt-Zoom) cameras. The proposed method employs a Linear Matrix Inequality (LMI) resolution approach and allows extra tunable constraints on the intrinsic parameters to be taken into account during the process of estimating these parameters. Furthermore, the considered constraints are simultaneously enforced in all views rather than in a single reference view. The results of our experiments show that the proposed approach allows for significant improvement in terms of accuracy and robustness when compared against state of the art methods.
LMI-based 2D-3D Registration: from Uncalibrated Images to Euclidean Scene
International audience; This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates , and two or more uncalibrated cameras. An unknown subset of the scanned points have their image projections detected and matched across images. The proposed approach assumes the cameras only known in some arbitrary projective frame and no calibration or autocalibration is required. The devised solution is based on a Linear Matrix Inequality (LMI) framework that allows simultaneously estimating the projective transformation relating the cameras to the scene and establishing 2D-3D correspondences without triangulating image points. The proposed LMI framewo…
Automatic object detection in point clouds based on knowledge guided algorithms
The modeling of real-world scenarios through capturing 3D digital data has been proven applicable in a variety of industrial applications, ranging from security, to robotics and to fields in the medical sciences. These different scenarios, along with variable conditions, present a challenge in discovering flexible appropriate solutions. In this paper, we present a novel approach based on a human cognition model to guide processing. Our method turns traditional data-driven processing into a new strategy based on a semantic knowledge system. Robust and adaptive methods for object extraction and identification are modeled in a knowledge domain, which has been created by purely numerical strate…
Localization of 2D Cameras in a Known Environment Using Direct 2D-3D Registration
International audience; In this paper we propose a robust and direct 2D-to- 3D registration method for localizing 2D cameras in a known 3D environment. Although the 3D environment is known, localizing the cameras remains a challenging problem that is particularly undermined by the unknown 2D-3D correspondences, outliers, scale ambiguities and occlusions. Once the cameras are localized, the Structure-from-Motion reconstruction obtained from image correspondences is refined by means of a constrained nonlinear optimization that benefits from the knowledge of the scene. We also propose a common optimization framework for both localization and refinement steps in which projection errors in one v…
Efficient Pruning LMI Conditions for Branch-and-Prune Rank and Chirality-Constrained Estimation of the Dual Absolute Quadric
International audience; We present a new globally optimal algorithm for self- calibrating a moving camera with constant parameters. Our method aims at estimating the Dual Absolute Quadric (DAQ) under the rank-3 and, optionally, camera centers chirality constraints. We employ the Branch-and-Prune paradigm and explore the space of only 5 parameters. Pruning in our method relies on solving Linear Matrix Inequality (LMI) feasibility and Generalized Eigenvalue (GEV) problems that solely depend upon the entries of the DAQ. These LMI and GEV problems are used to rule out branches in the search tree in which a quadric not satisfy- ing the rank and chirality conditions on camera centers is guarantee…
Stratified Autocalibration of Cameras with Euclidean Image Plane
International audience; This paper tackles the problem of stratified autocalibration of a moving camera with Euclidean image plane (i.e. zero skew and unit aspect ratio) and constant intrinsic parameters. We show that with these assumptions, in addition to the polynomial derived from the so-called modulus constraint, each image pair provides a new quartic polynomial in the unknown plane at infinity. For three or more images, the plane at infinity estimation is stated as a constrained polynomial optimization problem that can efficiently be solved using Lasserre's hierarchy of semidefinite relaxations. The calibration parameters and thus a metric reconstruction are subsequently obtained by so…
Affine camera calibration from homographies of parallel planes
This paper deals with the problem of retrieving the affine structure of a scene from two or more images of parallel planes. We propose a new approach that is solely based on plane homographies, calculated from point correspondences, and that does not require the recovery of the 3D structure of the scene. Neither vanishing points nor lines need to be extracted from the images. The case of a moving camera with constant intrinsic parameters and the one of cameras with possibly different parameters are both addressed. Extensive experiments with both synthetic and real images have validated our approach.
Estimation de la pose d'une caméra dans un environnement connu à partir d'un recalage 2D-3D
National audience; Nous proposons une méthode directe de recalage robuste 2D-3D permettant de localiser une caméra dans un environnement 3D connu. Il s'agit d'un problème rendu particulièrement difficile par l'absence de correspondances entre les points 3D du nuage et les points 2D. A cette difficulté, s'ajoute la différence d'échelle entre le nuage 3D connu et le nuage 3D reconstruit à partir d'images qui, de plus, peut contenir des points aberrants et des occultations. Notre méthode consiste en l'optimisation d'une fonctionnelle de manière itérative en deux étapes : estimation de la pose de la caméra et mise en correspondance 2D-3D. Ainsi, nous obtenons une méthode d'estimation conjointe …
Computer vision-based approach for rite decryption in old societies
International audience; This paper presents an approach to determine the spatial arrangement of bones of horses in an excavation site and perform the 3D reconstruction of the scene. The relative 3D positioning of the bones was computed exploiting the information in images acquired at different levels, and used to relocate provided 3D models of the bones. A novel semi-supervised approach was proposed to generate dense point clouds of the bones from sparse features. The point clouds were later matched with the given models using Iterative Closest Point (ICP).
2D-3D Camera Fusion for Visual Odometry in Outdoor Environments
International audience; Accurate estimation of camera motion is very important for many robotics applications involving SfM and visual SLAM. Such accuracy is attempted by refining the estimated motion through nonlinear optimization. As many modern robots are equipped with both 2D and 3D cameras, it is both highly desirable and challenging to exploit data acquired from both modalities to achieve a better localization. Existing refinement methods, such as Bundle adjustment and loop closing, may be employed only when precise 2D-to-3D correspondences across frames are available. In this paper, we propose a framework for robot localization that benefits from both 2D and 3D information without re…
A new method for linear affine self-calibration of stationary zooming stereo cameras
This paper presents a simple, yet effective, method to recover the affine structure of a scene from a (stereo) pair of stationary zooming cameras. The proposed method solely relies on point correspondences across images and no knowledge about the scene whatsoever is required. Our method exploits implicit properties of the projective camera matrices of zooming cameras and allows to estimate the affine structure of a scene by solving a linear system of equations. The 3D reconstruction results obtained by using our method, on both real and simulated data, have remarkably validated its feasibility.
Combining Mendonça-Cipolla self-calibration and scene constraints
International audience; In this paper, we propose a method that combines plane parallelism and the Mendonça/Cipolla self-calibration constraints. In our method each pair of images is treated independently and can therefore use a different pair of parallel planes not necessarily visible in the other views. While, for each pair of images, constraints on the singular values of the essential matrix provide two algebraic constraints on the intrinsic parameters, those we derive from plane parallelism have the advantage of providing two additional ones making the calibration of a no-skew camera possible from two images only.