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The result of our classification is a labeled 3D point cloud; each point assigned to one of the object classes natural ground, as- phalt ground, building, low vegetation or tree..
The task is to model the projection pro- cess of a camera system as the basis for a bundle adjustment for a multi-view camera system, which consists of mutually fixed single
In this paper we investigated a camera calibration algorithm based on the freely available Damped Bundle Adjustment Toolbox for Matlab that required initial values of only one
The proposed method first segments out 3D points belonging to the building fac¸ade from the 3D urban point cloud and then projects them onto a 2D plane parallel to the
In this chapter we report on a comparison of the two mentioned software packages, in particular the bundle adjustment results, the point clouds and the orthophotos
The test scenes can be used to evaluate the quality of the 3D re- construction (e.g. by comparing the resulting point cloud to the ground truth available for the two 3D
The calculated IOPs and EOPs initial values are used with other epipolar geometry constraints to calculate new refined IOPs and EOPs values which can be used to find
Instead of traditional image matching and applying bundle adjustment method to estimate transformation parameters, the IOPs and ROPs of multiple cameras are calibrated and
