Christoph Munkelt

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Measuring systems using fringe projection provide the possibility of very accurate touchless measurements. For the measurement of small objects compact devices are possible. However, in the case of very close distances between the optical system and the measuring object there is a considerable influence of the measuring distance to the lens distortion. This(More)
Next-best-view (NBV) planning is an important aspect for three-dimensional (3D) reconstruction within controlled environments, such as a camera mounted on a robotic arm. NBV methods aim at a purposive 3D reconstruction sustaining predefined goals and limitations. Up to now, literature mainly presents NBV methods for range sensors, model-based approaches or(More)
Feature tracking is an important task in computer vision, especially for 3D reconstruction applications. Such procedures can be run in environments with a controlled sensor, e.g. a robot arm with camera. This yields the camera parameters as special knowledge that should be used during all steps of the application to improve the results. As a first step, KLT(More)
The problem of planning the Next Best View (NBV) still poses many questions. However, the achieved methods and algorithms are hard to compare, since researchers use their own test objects for planning and reconstruction and compute specific quality measures. Consequently, these numbers make different statements about different objects. Thus, the quality of(More)
The widespread use of optical 3D measurement in inspection and prototyping continues to demand improved strategies for planning the next best view (NBV). During the scanning process, better viewpoints can be chosen and fewer views need to be taken, if a NBV algorithm is able to use available a-priori information about an object to-be-scanned. The proposed(More)
Multi-View Planning (MVP) for high fidelity three-dimensional (3D) reconstruction and inspection solves the problem of finding an efficient sequence of views allowing complete and high quality reconstruction of complex objects. Given a CAD model – or coarse 3D scan, or time of flight (TOF) 3D scan – of the object, our objective is to jointly evaluate(More)
Guided Kanade-Lucas-Tomasi (GKLT) feature tracking offers a way to perform KLT tracking for rigid scenes using known camera parameters as prior knowledge, but requires manual control of uncertainty. The uncertainty of prior knowledge is unknown in general. We present an extended modeling of GKLT that overcomes the need of manual adjustment of the(More)
Guided Kanade-Lucas-Tomasi (GKLT) tracking is a suitable way to incorporate knowledge about camera parameters into the standard KLT tracking approach for feature tracking in rigid scenes. By this means, feature tracking can benefit from additional knowledge about camera parameters as given by a controlled environment within a next-best-view (NBV) planning(More)