Multi-camera systems

1995

We present a method for camera calibration and metric reconstruction of the three-dimensional structure of scenes with several, possibly small and nearly planar objects from one or more images. We formulate the projection of object models explicitly according to the pin-hole camera model in order to be able to estimate the pose parameters for all objects as well as relative poses and the focal lengths of the cameras. This is accomplished by minimising a multivariate non-linear cost function. The only information needed is simple geometric object models, the correspondence between model and image features, and the correspondence of objects in the images if more than one view of the scene is used. Additionally, we present a new method for the projection of circles using projective invariants. Results using both simulated and real images are presented. keywords: Least-squares model fitting, model-based vision, 3-D reconstruction, camera calibration, projective invariants. 1 Introductio...

We present a method for camera calibration and metric reconstruction of the three-dimensional structure of scenes with several, possibly small and nearly planar objects from one or more images. We formulate the projection of object models explicitly according to the pin-hole camera model in order to be able to estimate the pose parameters for all objects as well as relative poses and the focal lengths of the cameras. This is accomplished by minimising a multivariate non-linear cost function. The only information needed is simple geometric object models, the correspondence between model and image features, and the correspondence of objects in the images if more than one view of the scene is used. Additionally, we present a new method for the projection of circles using projective invariants. Results using both simulated and real images are presented. keywords: Least-squares model fitting, model-based vision, 3-D reconstruction, camera calibration, projective invariants. 1

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)

We present a general algorithm for plane-based calibration that can deal with arbitrary numbers of views and calibration planes. The algorithm can simultaneously calibrate different views from a camera with variable intrinsic parameters and it is easy to incorporate known values of intrinsic parameters. For some minimal cases, we describe all singularities, naming the parameters that can not be estimated. Experimental results of our method are shown that exhibit the singularities while revealing good performance in non-singular conditions. Several applications of plane-based 3D geometry inference are discussed as well.

Digital Photogrammetry and Remote Sensing '95, 1995

The problem of determining spatial position and orientation of several cameras, knowing corresponding coordinates obtained by perspective projections onto the camera planes, is considered. Input data for calibration also include distances between some points in space. The calibration is carried out in two stages. In the first stage, position and orientation for pairs of images (stereo pairs) are determined. Every image is calibrated being included in one calibrated stereo pair. A special tree-like structure is built up as a result of the first stage. This structure contains input data and some information about links between the images. The calibration parameters obtained after each stereo pair calibration are considered as an initial approximation for the second stage. In this stage, the simultaneous calibration of all the images is performed to provide consistency and compatibility of final results. The proposed approach permits to avoid possible conflicts between calibration parameters and alleviates the problem of obtaining a good initial approximation for simultaneous calibration of multiple cameras. The experiments with real images produced promising results.

2007

This report addresses the problem of calibrating a single camera using several 3D ground control points and corresponding image points. Analytical and numerical approaches to approximate the desired camera model parameters are discussed.

2010

The classic perspective projection is mostly used when calibrating a camera. Although this approach is fairly developed and often suitable, it is not necessarily adequate to model any camera system like fish-eyes or catadioptrics. The perspective projection is not applicable when field of views reach 180° and beyond. In this case an appropriate model for a particular non perspective camera has to be used. Having an unknown camera system a generic camera model is required. This paper discusses a variety of parametric and generic camera models. These models will be validated subsequently using different camera systems. A unified approach of deriving initial parameter guesses for subsequent parameter optimisation is presented. Experimental results prove that generic camera models perform as accurate as a particular parametric model would do. Furthermore, there is no previous knowledge about the camera system needed.

2005

This paper presents four alternative ways of initializing camera parameters using essentially the same calibration tools (orthogonal wands) as nowadays popular 3D kinematic systems do. However, the key idea presented here is to sweep the volume with an orthogonal pair or triad of wands instead of a single one. The proposed methods exploit the orthogonality of the used wands and set up familiar linear constraints on certain entities of projective geometry. Extracted initial camera parameters values are closer to the refined ones, which should generally assure faster and safer convergence during the refinement procedure. Even without refinement, sometimes not necessary, reconstruction results using our initial sets are better than using commonly obtained initial values. Besides, the entire calibration procedure is shortened since the usual two calibration phases become one.

Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)

The 30 reconstruction's quality of multiple-camera acquisition systems is strongly influenced by the accuracy of the camera calibration procedure. The acquisition of long sequences is, in fact, very sensitive to mechanical shocks, vibrations and thermal changes on cameras and supports, as they could result in a significant drip of the camera parameters. In this paper we propose a technique which is able to keep track of the camera parameters and, whenever possible, to correct them accordingly. This technique does not need any a-priori knowledge or test objects to be placed in the scene, but exploits features that are already present in the scene itse& In fact it pegorms an accurate detection, matching and back-projection of luminance corners and spots in the scene space. Experimental results on real sequences are reported in order to prove the ability of the proposed technique to detect a change in the calibration and to re-calibrate the camera setup with an accuracy that depends on the number of available feature points.

Computer Vision and Image Understanding, 1996

tures are extracted from the image by means of standard image analysis techniques. These features are generally This paper presents an original approach to the problem of camera calibration using a calibration pattern. It consists of points or lines, but conics can also be used . Then, directly searching for the camera parameters that best project they are given as input to an optimization process which three-dimensional points of a calibration pattern onto intensity searches for the projection parameters P that best project edges in an image of this pattern, without explicitly extracting the three-dimensional model onto them. We will not describe in detail the different methods that ima of the intensity gradient or zero-crossings of the Laplacian, have been developed. Detailed reviews of the main existing we express the whole calibration process as a one-stage optimiapproaches can be found in [15, 16,. We just remark zation problem. A classical iterative optimization technique is that the approaches can be classified into several categoused in order to solve it. Our approach is different from the ries, with respect to: • the camera model: most existing calibration methods camera parameters. Thus, our approach is easier to implement assume that the camera follows the pinhole model. Some and to use, less dependent on the type of calibration pattern of them (mostly in photogrammetry) consider additional that is used, and more robust. First, we describe the details of parameters that model image distorsions. A good study of the approach. Then, we show some experiments in which two the different geometrical distorsion models can be found implementations of our approach and two classical two-stage in . approaches are compared. Tests on real and synthetic data • the optimization process: linear optimization processes allow us to characterize our approach in terms of convergence, sensitivity to the initial conditions, reliability, and accuracy. are often used in computer vision because they are faster

2002

A new technique for the determination of extrinsic and intrinsic camera parameters is presented. Instead of searching for a limited number of discrete feature points of the calibration test object, the entire image captured with the camera is exploited to robustly determine the unknown parameters. Shape and texture of the test object are described by a 3-D computer graphics model. With this 3-D representation, synthetic images are rendered and matched with the original frames in an analysis by synthesis loop. Therefore, arbitrary test objects with sophisticated patterns can be used to determine the camera settings. The scheme can easily be extended to deal with multiple frames for a higher intrinsic parameter accuracy.