[PDF] Autocalibration from Planar Scenes | Semantic Scholar (2024)

This work shows that when there is some control over the motion of the camera, a fast linear solution is available without these restrictions, and shows the algorithm to be simple, fast, and accurate.

A method for identifying the major planar structures in a scene from images and another method to recognize parallel pairs of planes whenever these are available and a refinement method which is capable of simultaneously incorporating plane parallelism and perpendicularity constraints in the autocalibration process are proposed.

A simple approach to combining scene and auto-calibration constraints for the calibration of cameras from single views and stereo pairs and examples of various cases of constraint combination and degeneracy as well as computational techniques are presented.

This paper focuses on self-calibration from unknown planar structures proposing a method exploiting the consistent set of collineations which can be used for several applications such as mosaicing, reconstruction, and self-Calibration.

It is shown that for the autocalibration of the focal length and aspect ratio, the evolutionary method achieves results comparable to published methods but is simpler to implement and is efficient enough to handle larger image sequences.

A self-calibration method for a CCD camera with varying intrinsic parameters based on an unknown planar scene that reduces the number of images needed to estimate the parameters of the camera used by minimizing a non-linear cost function in a two-step procedure.

A unified theory for autocalibration of a moving camera that provides not only a common theoretical framework, but also suggests unified parameterisation schema for estimation procedures.

This paper shows theoretically that the calibration is unique up to an orthogonal transformation under the assumption that the skew of the camera is zero, and suggests a method for obtaining the calibration parameters by searching the space of the principal point.

The main idea of the method addressed in this paper is the use of interest points automatically detected in two images of an unknown 3D scene to self-calibrate the camera.

A very simple and effective method for eliminating the degeneracy inherent in a planar scene, and its performance in a useful application - binocular self-calibration.

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The author describes a new method for camera autocalibration and scaled Euclidean structure and motion, from three or more views taken by a moving camera with fixed but unknown intrinsic parameters, based on a general constrained optimization technique-sequential quadratic programming-that may well be useful in other vision problems.

There is no epipolar structure since all images are taken from the same point in space and determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur.

A stratified approach is proposed which gradually retrieves the metric calibration of the camera setup using a constraint which can be formulated between any two arbitrary images of the sequence.

A reconstruction method that allows to vary the focal length and is resistant to noise, and in which case also reconstruction from a moving rig becomes possible even for pure translation.

A mechanism of image stabilization in which the rotational disparity is iteratively canceled allows a better estimation of the focus of expansion, and simplifies different aspects of the analysis of the equations: structure from motion equation, analysis of ambiguity, geometrical interpretation of the motion equation.

It is shown, using experiments with noisy data, that it is possible to calibrate a camera just by pointing it at the environment, selecting points of interest and then tracking them in the image as the camera moves.

This paper describes a method to upgrade projective reconstruction to affine and to metric reconstructions using rigid general motions of a stereo rig, and carries out a theoretical error analysis which quantify the relative importance of the accuracies of projective- to-affine conversion and affine-to-Euclidean conversion.

The self-calibration technique described in this article is the generalization to a large number of images of the algorithm developped by Luong and Faugeras based on the Kruppa equations.

This paper addresses the problem of self-calibration and metric reconstruction from one unknown motion of an uncalibrated stereo rig and finds that redundancy of the information contained in a sequence of stereo images makes this method more robust than using a sequences of monocular images.

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