By Christian Wöhler
This crucial textual content introduces the rules of third-dimensional desktop imaginative and prescient and describes fresh contributions to the sphere. absolutely revised and up-to-date, this much-anticipated re-creation reports a number triangulation-based equipment, together with linear and package adjustment dependent methods to scene reconstruction and digicam calibration, stereo imaginative and prescient, aspect cloud segmentation, and pose estimation of inflexible, articulated, and versatile items. additionally lined are intensity-based ideas that evaluation the pixel gray values within the photo to deduce three-d scene constitution, and aspect unfold functionality dependent ways that take advantage of the influence of the optical approach. The textual content exhibits how equipment which combine those ideas may be able to elevate reconstruction accuracy and robustness, describing purposes in commercial caliber inspection and metrology, human-robot interplay, and distant sensing.
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Additional info for 3D Computer Vision: Efficient Methods and Applications
Camera calibration without a dedicated calibration rig, thus exclusively relying on feature points extracted from a set of images of a scene of unknown geometry and the established correspondences between them, is termed ‘self-calibration’. 1 Projective Reconstruction: Determination of the Fundamental Matrix This section follows the presentation by Hartley and Zisserman (2003). The first step of self-calibration from multiple views of an unknown static scene is the determination of the fundamental matrix F between image pairs as defined in Sect.
U1 v2 .. v1 v2 .. v2 .. u1 .. v1 .. ⎤ 1 .. ⎦ (n) u(n) 2 v1 u(n) 2 (n) u(n) 1 v2 v1(n) v2(n) v2(n) u(n) 1 v1(n) 1 (1) (1) u1 u2 ⎢ .. Gf = ⎣ . (n) u(n) 1 u2 (1) (1) (1) (1) (1) (1) (1) = 0. 52). A unique solution (of unknown scale) is directly obtained if the coefficient matrix G is of rank 8. However, if is it assumed 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.
Hence, it extracts a number of most prominent features, equal to the number of corners in the calibration rig, by means of cross-correlation matching (Aschwanden, 1993). Subsequently, an outlier detection is performed based on a Hough transform (Jähne, 2005) and geometric constraint evaluation. Upon this, the lines are approximated by least-squares methods. Independent of the previously detected features, the line intersections are used as coarse corner guesses, and a maximum search followed by fitting a paraboloid to the correlation coefficients is performed.