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.

Real-time depth camera tracking with CAD models and ICP

In recent years, depth cameras have been widely utilized in camera tracking for augmented and mixed reality. Many of the studies focus on the methods that generate the reference model simultaneously with the tracking and allow operation in unprepared environments. However, methods that rely on predefined CAD models have their advantages. In such methods, the measurement errors are not accumulated to the model, they are tolerant to inaccurate initialization, and the tracking is always performed directly in reference model's coordinate system. In this paper, we present a method for tracking a depth camera with existing CAD models and the Iterative Closest Point (ICP) algorithm. In our approach, we render the CAD model using the latest pose estimate and construct a point cloud from the corresponding depth map. We construct another point cloud from currently captured depth frame, and find the incremental change in the camera pose by aligning the point clouds. We utilize a GPGPU-based implementation of the ICP which efficiently uses all the depth data in the process. The method runs in real-time, it is robust for outliers, and it does not require any preprocessing of the CAD models. We evaluated the approach using the Kinect depth sensor, and compared the results to a 2D edge-based method, to a depth-based SLAM method, and to the ground truth. The results show that the approach is more stable compared to the edge-based method and it suffers less from drift compared to the depth-based SLAM.

Fitting 3D morphable models using implicit representations

We consider the problem of approximating the 3D scan of a real object through an affine combination of examples. Common approaches depend either on the explicit estimation of point-to-point correspondences or on 2-dimensional projections of the target mesh; both present drawbacks. We follow an approach similar to [IF03] by representing the target via an implicit function, whose values at the vertices of the approximation are used to define a robust cost function. The problem is approached in two steps, by approximating first a coarse implicit representation of the whole target, and then finer, local ones; the local approximations are then merged together with a Poisson-based method. We report the results of applying our method on a subset of 3D scans from the Face Recognition Grand Challenge v.1.0.

Algorithms For Automatic And Robust Registration Of 3D Head Scans

wo methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function. Thin-plate spline interpolation is then used to deform the template mesh and finally the scan is resampled in the topology of the deformed template. The second algorithm employs a morphable shape model, which can be computed from a database of laser-scans using the first algorithm. It directly optimizes pose and shape of the morphable model. The use of the algorithm with PCA mixture models, where the shape is split up into regions each described by an individual subspace, is addressed. Mixture models require either blending or regularization strategies, both of which are described in detail. For both algorithms, strategies for filling in missing geometry for incomplete laser-scans are described. While an interpolation-based approach can be used to fill in small or smooth regions, the model-driven algorithm is capable of fitting a plausible complete head mesh to arbitrarily small geometry, which is known as "shape completion". The importance of regularization in the case of extreme shape completion is shown.

3D printing and its applications

Eine zunehmende Anzahl von Artikeln in Publikumszeitschriften und Journalen rückt die direkte Herstellung von Bauteilen und Figuren immer mehr in das Bewusstsein einer breiten Öffentlichkeit. Leider ergibt sich nur selten ein einigermaßen vollständiges Bild davon, wie und in welchen Lebensbereichen diese Techniken unseren Alltag verändern werden. Das liegt auch daran, dass die meisten Artikel sehr technisch geprägt sind und sich nur punktuell auf Beispiele stützen. Dieser Beitrag geht von den Bedürfnissen der Menschen aus, wie sie z.B. in der Maslow’schen Bedürfnispyramide strukturiert dargestellt sind und unterstreicht dadurch, dass 3D Printing (oder Additive Manufacturing resp. Rapid Prototyping) bereits alle Lebensbereiche erfasst hat und im Begriff ist, viele davon zu revolutionieren.

3D Drucken und die Anwendungen

Eine zunehmende Anzahl von Artikeln in Publikumszeitschriften und Journalen rückt die direkte Herstellung von Bauteilen und Figuren immer mehr in das Bewusstsein einer breiten Öffentlichkeit. Leider ergibt sich nur selten ein einigermaßen vollständiges Bild davon, wie und in welchen Lebensbereichen diese Techniken unseren Alltag verändern werden. Das liegt auch daran, dass die meisten Artikel sehr technisch geprägt sind und sich nur punktuell auf Beispiele stützen. Dieser Beitrag geht von den Bedürfnissen der Menschen aus, wie sie z.B. in der Maslow’schen Bedürfnispyramide strukturiert dargestellt sind und unterstreicht dadurch, dass 3D Printing (oder Additive Manufacturing resp. Rapid Prototyping) bereits alle Lebensbereiche erfasst hat und im Begriff ist, viele davon zu revolutionieren.