Download 3D Reconstruction from Multiple Images by Theo Moons, Luc van Gool, Maarten Vergauwen PDF

By Theo Moons, Luc van Gool, Maarten Vergauwen

3D Reconstruction from a number of pictures, half 1: ideas discusses and explains how to extract three-d (3D) types from simple photos. particularly, the 3D details is acquired from photos for which the digicam parameters are unknown. the foundations underlying such uncalibrated structure-from-motion tools are defined. First, a brief overview of 3D acquisition applied sciences places such tools in a much wider context and highlights their vital benefits. Then, the particular conception in the back of this line of analysis is given. The authors have attempted to maintain the textual content maximally self-contained, for this reason additionally averting hoping on an intensive wisdom of the projective innovations that typically look in texts approximately self-calibration 3D tools. particularly, mathematical factors which are extra amenable to instinct are given. the reason of the idea contains the stratification of reconstructions acquired from snapshot pairs in addition to metric reconstruction at the foundation of greater than photographs mixed with a few extra wisdom concerning the cameras used. 3D Reconstruction from a number of photographs, half 1: ideas is the 1st of a three-part Foundations and traits instructional in this subject written by way of an identical authors. half II will concentrate on simpler information regarding the right way to enforce such uncalibrated structure-from-motion pipelines, whereas half III will define an instance pipeline with extra implementation matters particular to this actual case, and together with a consumer advisor.

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Extra resources for 3D Reconstruction from Multiple Images

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Identifying the vanishing points of 4 independent directions in an image pair is rarely possible. More often, one has three dominant directionsFortuntalely, typically orthogonal to each other. This is the case for most built-up environments. Fortunately, there is one direction which is always available, namely the line passing through the positions C1 and C2 in the scene. The vanishing points of this line in the images are the intersection of the line with each image plane. But these are just the epipoles.

The model described above puts the radial distortion parameters between the extrinsic and linear intrinsic parameters of the camera. e. a 3D points is first projected into the image via the linear model and then undistorted. Conceptually the latter model is less suited than the one used here because putting the distortion at the end makes it dependent on the internal parameters, especially on the focal length. This means one has to re-estimate the radial distortion parameters every time the focal length changes.

Measure the 3D positions of the markers on this object. • Take one or more (depending on the algorithm) pictures of the object with the camera to be calibrated. • Extract and identify the markers in the image. • Fit a linearized calibration model to the 3D-2D correspondences found in the previous step. • Improve the calibration with a non-linear optimization step. Tsai’s and Zhang’s methods As mentioned, both 2D and 3D calibration objects can be used to perform the calibration. In his seminal article Tsai [81] describes an algorithm to calibrate a camera with either 2D or 3D calibration objects.

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