Grundlagen des Rapid Prototyping

Generative Verfahren sind seit etwa 1987 in den USA und seit etwa 1990 in Europa und Deutschland in Form von Rapid Prototyping Verfahren bekannt und haben sich in dieser Zeit von eher als exotisch anzusehenden Modellbauverfahren zu effizienten Werkzeugen für die Beschleunigung der Produktentstehung gewandelt. Mit der Weiterentwicklung der Verfahren und insbesondere der Materialien wird mehr und mehr das Feld der direkten Anwendung der Rapid Technologie zur Fertigung erschlossen. Rapid Technologien werden daher zum Schlüssel für neue Konstruktionssystematiken und Fertigungsstrategien. Die Anwendertagung Rapid.Tech befasst sich mit den neuen Verfahren zur direkten Produktion und den daraus erwachsenden Chancen für Entwickler und Produzenten. Die Kenntnis der Rapid Prototyping Verfahren wird bei den meisten Fachvorträgen auf der Rapid.Tech vorausgesetzt. Für diejenigen, die sich bisher mit generativen Verfahren noch nicht beschäftigt haben, oder die ihre Grundkenntnisse schnell auffrischen wollen, haben wir die folgenden Zusammenfassung der Grundlagen der generativen Fertigungstechnik, der heutigen Rapid Prototyping Verfahren, zusammengestellt.

Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-camera reconstructions is essential for successful camera motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.

Visual Fixation for 3D Video Stabilization

Visual fixation is employed by humans and some animals to keep a specific 3D location at the center of the visual gaze. Inspired by this phenomenon in nature, this paper explores the idea to transfer this mechanism to the context of video stabilization for a handheld video camera. A novel approach is presented that stabilizes a video by fixating on automatically extracted 3D target points. This approach is different from existing automatic solutions that stabilize the video by smoothing. To determine the 3D target points, the recorded scene is analyzed with a stateof- the-art structure-from-motion algorithm, which estimates camera motion and reconstructs a 3D point cloud of the static scene objects. Special algorithms are presented that search either virtual or real 3D target points, which back-project close to the center of the image for as long a period of time as possible. The stabilization algorithm then transforms the original images of the sequence so that these 3D target points are kept exactly in the center of the image, which, in case of real 3D target points, produces a perfectly stable result at the image center. Furthermore, different methods of additional user interaction are investigated. It is shown that the stabilization process can easily be controlled and that it can be combined with state-of-theart tracking techniques in order to obtain a powerful image stabilization tool. The approach is evaluated on a variety of videos taken with a hand-held camera in natural scenes.