3D computer vision: efficient methods and applications by Christian Wöhler

3D computer vision: efficient methods and applications by Christian Wöhler

By Christian Wöhler

This ebook presents an advent to the principles of three-d desktop imaginative and prescient and describes contemporary contributions to the sphere. Geometric equipment comprise linear and package adjustment dependent techniques to scene reconstruction and digicam calibration, stereo imaginative and prescient, aspect cloud segmentation, and pose estimation of inflexible, articulated, and versatile gadgets. Photometric thoughts overview the depth distribution within the photo to deduce 3-dimensional scene constitution, whereas real-aperture techniques make the most the habit of the purpose unfold functionality. it really is proven how the combination of numerous equipment raises reconstruction accuracy and robustness. purposes eventualities comprise commercial caliber inspection, metrology, human-robot-interaction, and distant sensing.

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Similarly, vanishing points fulfilling the orthogonality condition can be used for computing the IAC ω . A pair of vanishing points S v˜ 1 and S v˜ 2 corresponding to orthogonal directions in the scene are shown by Hartley and Zisserman (2003) to represent conjugate points with respect to ω , thus fulfilling the relation S T v˜ 1 ω S v˜ 2 = 0. 84) The five independent parameters defining the matrix ω can be obtained based on the linear constraints imposed by three mutually orthogonal vanishing points.

57) . . . . .  f = 0.  (n) (n) (n) (n) (n) (n) (n) (n) (n) (n) (n) (n) u 1 u 2 u 2 v1 u 2 u 1 v2 v1 v2 v2 u 1 v1 1 The matrix F can only be obtained up to a scale factor because Eq. 57) is a homogeneous equation. A solution which is unique up to scale is directly obtained if the coefficient matrix G is of rank 8. However, if we assume that the established point correspondences are not exact due to measurement noise, the rank of the coefficient matrix G is 9 even if only eight point correspondences are taken into account, and the accuracy of the solution for F generally increases if still more point correspondences are regarded.

However, not every distribution of scene points is equally well suited for an accurate determination of camera parameters based on self-calibration—as an example, many linear methods fail if the scene points do not occupy a volume in space. As a consequence, during the camera calibration phase a “cooperative” scene from which scene points and their mutual correspondences can be extracted at high accuracy and reliability is highly desirable. Later on, once the intrinsic camera parameters (and the relative extrinsic camera transformations in the case of multiple cameras) are known, the scenes to be reconstructed may become less cooperative.

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