A Factorization Based Self-Calibration for Radially Symmetric Cameras

The paper proposes a novel approach for planar selfcalibration of radially symmetric cameras. We model these camera images using notions of distortion center and concentric distortion circles around it. The rays corresponding to pixels lying on a single distortion circle form a right circular cone. Each of these cones is associated with two unknowns; optical center and focal length (opening angle). In the central case, we consider all distortion circles to have the same optical center, whereas in the non-central case they have different optical centers lying on the same optical axis. Based on this model we provide a factorization based self-calibration algorithm for planar scenes from dense image matches. Our formulation provides a rich set of constraints to validate the correctness of the distortion center. We also propose possible extensions of this algorithm i

Geometrically-driven underground camera modeling and calibration with coplanarity constraints for Boom-type roadheader

The conventional calibration methods based on perspective camera model are not suitable for underground camera with two-layer glasses, which is specially designed for explosion-proof and dust removal in coal mine. The underground camera modeling and calibration algorithms are urgently needed to improve the precision and reliability of underground visual measurement system. This paper presents a novel geometrically-driven underground camera calibration algorithm for Boom-type roadheader. The underground camera model is established under coplanarity constraints, considering explicitly the impact of refraction triggered by the two-layer glasses and deriving the geometrical relationship of equivalent collinearity equations. On this basis, we perform parameters calibration based on a geometrically-driven calibration model, which is a 2D-2D correspondences between the image points and object coordinates of the plannar target. A hybrid LM-PSO algorithm is further proposed in terms of the dynamic combination of the Levenberg-Marqurdt (LM) and Particle Swarm Optimization (PSO), which optimize the underground camera calibration results by minimizing the error of the nonlinear underground camera model. The experiment results demonstrate that the pose errors caused by the two-layer glass refraction are well corrected by the proposed method. The accuracy of the cutting-head pose estimation has increased by 55.73%, meeting the requirements of underground excavations

Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve

Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone.Comment: 15 pages, 12 figures, accepted to CVPR 2020 as an ora

Reconstruction active et passive en vision par ordinateur

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Calibration of Cameras with Radially Symmetric Distortion

International audienceWe present a theory and algorithms for plane-based calibration and pose recovery of general radially distorted cameras. By this we understand cameras that have a distortion center and an optical axis such that the projection rays of pixels lying on a circle centered on the distortion center, form a right cone centered on the optical axis. The camera is said to have a singular viewpoint (SVP) if all such view cones have the same vertex (the optical center), otherwise we speak of non-SVP, and each view cone may have its own optical center on the optical axis. This model encompasses the classical radial distortion model, fisheyes, most central or non-central catadioptric cameras, but also cameras with radially symmetric caustics. Calibration consists in the estimation of the distortion center, the opening angles of all view cones and their optical center. We present two approaches of computing a full calibration from dense correspondences of a single or multiple planes with known euclidean structure. The first one is based on a geometric constraint linking view cones and associated ellipses in the calibration plane; calibration of the view cones can be solved by determining the closest point to a set of hyperbolas. The second approach uses existing plane-based calibration methods to directly calibrate individual view cones. A simple distortion correction algorithm for calibrated SVP images is given. Preliminary experiments show convincing results

Calibration of Cameras with Radially Symmetric Distortion

International audienceWe present a theory and algorithms for plane-based calibration and pose recovery of general radially distorted cameras. By this we understand cameras that have a distortion center and an optical axis such that the projection rays of pixels lying on a circle centered on the distortion center, form a right cone centered on the optical axis. The camera is said to have a singular viewpoint (SVP) if all such view cones have the same vertex (the optical center), otherwise we speak of non-SVP, and each view cone may have its own optical center on the optical axis. This model encompasses the classical radial distortion model, fisheyes, most central or non-central catadioptric cameras, but also cameras with radially symmetric caustics. Calibration consists in the estimation of the distortion center, the opening angles of all view cones and their optical center. We present two approaches of computing a full calibration from dense correspondences of a single or multiple planes with known euclidean structure. The first one is based on a geometric constraint linking view cones and associated ellipses in the calibration plane; calibration of the view cones can be solved by determining the closest point to a set of hyperbolas. The second approach uses existing plane-based calibration methods to directly calibrate individual view cones. A simple distortion correction algorithm for calibrated SVP images is given. Preliminary experiments show convincing results

Calibration of Cameras with Radially Symmetric Distortion

We present algorithms for plane-based calibration of general radially distorted cameras. By this we understand cameras that have a distortion center and an optical axis such that the projection rays of pixels lying on a circle centered on the distortion center, form a right viewing cone centered on the optical axis. The camera is said to have a single viewpoint (SVP) if all such viewing cones have the same apex (the optical center), otherwise we speak of NSVP cases. This model encompasses the classical radial distortion model [4], fisheyes and most central or non-central catadioptric cameras. Calibration consists in the estimation of the distortion center, the opening angles of all viewing cones and their optical centers. We present two approaches of computing a full calibration from dense correspondences of a single or multiple planes with known Euclidean structure. The first one is based on a geometric constraint linking viewing cones and their intersections with the calibration plane (conic sections). The second approach is an homography-based method. Experiments using simulated and a broad variety of real cameras show great stability. Furthermore, we provide a comparison with Hartley-Kang’s algorithm [14], which however can not handle such a broad variety of camera configurations, showing similar performance