By Theo Moons, Luc van Gool, Maarten Vergauwen
3D Reconstruction from a number of pictures, half 1: rules discusses and explains how you can extract third-dimensional (3D) versions from simple pictures. particularly, the 3D info is bought from pictures for which the digicam parameters are unknown. the foundations underlying such uncalibrated structure-from-motion tools are defined. First, a brief evaluate of 3D acquisition applied sciences places such tools in a much wider context and highlights their very important benefits. Then, the particular idea at the back of this line of study is given. The authors have attempted to maintain the textual content maximally self-contained, consequently additionally fending off hoping on an intensive wisdom of the projective recommendations that sometimes seem in texts approximately self-calibration 3D equipment. relatively, mathematical motives which are extra amenable to instinct are given. the reason of the speculation contains the stratification of reconstructions got from snapshot pairs in addition to metric reconstruction at the foundation of greater than pictures mixed with a few extra wisdom in regards to the cameras used. 3D Reconstruction from a number of photos, half 1: rules is the 1st of a three-part Foundations and tendencies instructional in this subject written via a similar authors. half II will specialise in more effective information regarding how you can enforce such uncalibrated structure-from-motion pipelines, whereas half III will define an instance pipeline with additional implementation matters particular to this actual case, and together with a consumer consultant.
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Extra info for 3D Reconstruction from Multiple Images, Part 1: Principles
So, [e2 ]× F is a 3 × 3-matrix which when premultiplied with [e2 ]× yields a non-zero scalar multiple of the fundamental matrix F. 5 Two Image-Based 3D Reconstruction Up-Close 347 could be a candidate for the unknown matrix A. Unfortunately, as both the fundamental matrix F and [e2 ]× have rank 2, the matrix [e2 ]× F is not invertible as A ought to be. But, recall from the previous section that two matrices A1 and A2 satisfying F = [e2 ]× A1 and F = [e2 ]× A2 are related by A1 = A2 + e2 aT for some three-vector a ∈ R3 .
In the remainder of the text we will assume that radial distortion has been removed from all images if present, unless stated otherwise. 6 Explicit Camera Calibration As explained before a perspective camera can be described by its internal and external parameters. The process of determining these parameters is known as camera calibration. Accordingly, we make a distinction between internal or internal calibration and external or external calibration, also known as pose estimation. For the determination of all parameters one often uses the term complete calibration.
12) of the second camera, yields T ρ2 m2 = ρ1 K2 RT2 R1 K−1 1 m1 + K2 R2 (C1 − C2 ). 14) The last term in this equation corresponds to the projection e2 of the position C1 of the ﬁrst camera in the second image: ρe2 e2 = K2 RT2 (C1 − C2 ). 15) e2 is called the epipole of the ﬁrst camera in the second image. 11) in the second image. 3 that R1 K−1 1 m1 is the direction vector of the projecting ray of m1 with respect to the world frame. In the camera-centered reference frame of the second camera, the coordinates of this vector are RT2 R1 K1 −1 m1 .