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

By Christian Wöhler

This publication presents an advent to the rules of 3-dimensional desktop imaginative and prescient and describes fresh contributions to the sphere. Geometric equipment comprise linear and package deal adjustment dependent methods to scene reconstruction and digital camera calibration, stereo imaginative and prescient, aspect cloud segmentation, and pose estimation of inflexible, articulated, and versatile items. Photometric thoughts assessment the depth distribution within the snapshot to deduce third-dimensional scene constitution, whereas real-aperture techniques take advantage of the habit of the purpose unfold functionality. it really is proven how the combination of a number of tools raises reconstruction accuracy and robustness. purposes eventualities comprise commercial caliber inspection, metrology, human-robot-interaction, and distant sensing.

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Another drawback is the fact that the optimisation scheme is not equivalent to bundle adjustment. While bundle adjustment minimises the backprojection error in the image plane, Eq. 30) illustrates that the DLT method minimises the error of the backprojected scaled pixel coordinates (ui /Qi , vi /Qi ). It is not guaranteed that this somewhat arbitrary error measure is always a reasonable choice. 3 The Camera Calibration Method by Tsai (1987) In contrast to the DLT method, the camera calibration method by Tsai (1987) minimises the backprojection error in the image plane.

53), however, does not generally satisfy the orthonormality constraints imposed on a rotation matrix. For initialisation of the subsequent nonlinear bundle adjustment procedure, Zhang (1998) therefore describes a method to estimate the rotation matrix which is closest to a given 3 × 3 matrix in terms of the Frobenius norm. Similar to the DLT method, the intrinsic and extrinsic camera parameters computed so far have been obtained by minimisation of an algebraic error measure which is not physically meaningful.

L16 is obtained according to  x1 y1 z1 1 −u1 x1 −u1 y1 −u1 z1 Q1 Q1 Q1 Q1 0 0 0 0 Q1 Q1 Q1  0 0 0 0 x1 y1 z1 1 −v1 x1 −v1 y1 −v1 z1  Q1 Q1 Q1 Q1 Q1 Q1 Q1  . . . . .. ..  . . . . . .  . . . .  xN yN zN 1  QN QN QN QN 0 0 0 0 −uQNNxN −uQNNyN −uQNNzN 0 0 0 0 QxNN QyNN QzNN Q1N −vQNNxN −vQNNyN −vQNNzN  u1   ξ1 r12 ξ1 r14 ξ1 r16 r12 + 2ξ12 η1 ξ1    Qv11  L η1 r12 η1 r14 η1 r16 η1 ξ1 r12 + 2η12   Q1  1    ..   .. .. .. 30)  .  =  . . .     u N  ξN rN2 ξN rN4 ξN rN6 rN2 + 2ξN2 ηN ξN  L16  QN  vN ηN rN2 ηN rN4 ηN rN6 ηN ξN rN2 + 2ηN2 Q N Eq.

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