Automation of camera calibration is facilitated by recording coded 2D patterns. Our toolbox for automatic camera calibration using images of simple chess-board patterns is freely available on the Internet. But it is unsuitable for stereo-cameras whose calibration implies recovering camera geometry and their true-to-scale relative orientation. In contrast to all reported methods requiring additional specific coding to establish an object space coordinate system, a toolbox for automatic stereo-camera calibration relying on ordinary chess-board patterns is presented here. First, the camera calibration algorithm is applied to all image pairs of the pattern to extract nodes of known spacing, order them in rows and columns, and estimate two independent camera parameter sets. The actual node correspondences on stereo-pairs remain unknown. Image pairs of a textured 3D scene are exploited for finding the fundamental matrix of the stereo-camera by applying RANSAC to point matches established with the SIFT algorithm. A node is then selected near the centre of the left image; its match on the right image is assumed as the node closest to the corresponding epipolar line. This yields matches for all nodes (since these have already been ordered), which should also satisfy the 2D epipolar geometry. Measures for avoiding mismatching are taken. With automatically estimated initial orientation values, a bundle adjustment is performed constraining all pairs on a common (scaled) relative orientation. Ambiguities regarding the actual exterior orientations of the stereo-camera with respect to the pattern are irrelevant. Results from this automatic method show typical precisions not above ¼ pixels for 640x480 web cameras.
In this paper, a fully automatic 3D surface scanner, from point collection to point cloud registration and smoothing, is presented. The system is composed by a camera pair, which is calibrated automatically, and a hand-held laser plane. On epipolar images, generated from the stereo-frames taken as the object is being swept over by the laser plane, the search for point correspondences is reduced to identifying intersections of image rows with the recorded laser profiles. A variation of fitting Gaussian curves to the gray-value data along epipolar lines allows estimating peak positions by also using information from the vicinity of the peak. 3D reconstruction by simple stereovision is strengthened geometrically by imposing additional coplanarity constraints. All unknowns for a scanning position are estimated simultaneously in a single iterative adjustment. In order to register point clouds from different scan positions, the ICP algorithm is applied. Initial values for ICP are obtained automatically by using images acquired from adjacent scanning positions. For this, SIFT points on images of overlapping scans are extracted, matched and related to the scans to provide 3D point correspondences, which allow the required approximate 3D registration. The tools employed here for surface smoothing are also presented. Finally, examples are given to illustrate the performance of described methods.
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