This paper proposes a novel approach to registering deformations of 3D non-rigid objects for Augmented Reality applications. Our prototype is able to handle different types of objects in real-time regardless of their geometry and appearance (with and without texture) with the support of an RGB-D camera. During an automatic offline stage, the model is processed in order to extract the data that serves as input for a physics-based simulation. Using its output, the deformations of the model are estimated by considering the simulated behaviour as a constraint. Furthermore, our framework incorporates a tracking method based on templates in order to detect the object in the scene and continuously update the camera pose without any user intervention. Therefore, it is a complete solution that extends from tracking to deformation formulation for either textured or untextured objects regardless of their geometrical shape. Our proposal focuses on providing a correct visual with a low computational cost. Experiments with real and synthetic data demonstrate the visual accuracy and the performance of our approach.

A real-time Augmented Reality system that assists in maintenance is presented (Figure 1). The novelty of the proposed system relies on its ability of being a complete framework that is self supplied. All the data is extracted automatically during an offlne phase from a collection of 3D parts that are provided as untextured 3D triangle meshes. Our disassembly planning module computes the assembly/disassembly sequence finding collision-free trajectories [1]. Moreover, some basic geometric features, such as edges and junctions, are extracted to address the tracking problem. Additionally, these geometric features let us perform the 3D model recognition, which solves the first camera pose problem and performs the recovery in case of tracking failure [3]. During the online phase, these geometric features are used to build a markerless tracking that updates continuously the camera pose from a monocular image. This markerless tracking combines an edge tracker, a point based tracker and a 3D particle filter to offer a robust 3D tracking against undesirable conditions [2]. Once the camera pose has been computed, the system offers an augmented instruction, indicating how to assemble/disassemble the next part. Thus, the proposed AR system is a complete framework that runs in real-time and tries to facilitate the work of workers, replacing the paper-based documentation.

ABSTRACT Marker systems are a widely used optical tracking method that does not support occlusions. Thus, this paper proposes a new marker design to overcome the problem of marker occlusions. It is highly adaptable, because it can be used by any marker tracking system that uses its central area to codify the digital identification. Our proposal takes advantage of an untapped frame to place some textures that will be tracked during marker occlusion. In addition, these textures are customizable, which lets users make their own designs. Two tracking methods are combined to offer a robust tracking, updating the six degrees of freedom of the camera in real time. The first one is a fast technique based on temporal coherence, whereas the second one is a robust technique based on appearance, which is used as a recovery mode. Copyright © 2012 John Wiley & Sons, Ltd.

This paper proposes a new real-time Augmented Reality based tool to help in disassembly for maintenance operations. This tool provides workers with augmented instructions to perform maintenance tasks more efficiently. Our prototype is a complete framework characterized by its capability to automatically generate all the necessary data from input based on untextured 3D triangle meshes, without requiring additional user intervention. An automatic offline stage extracts the basic geometric features. These are used during the online stage to compute the camera pose from a monocular image. Thus, we can handle the usual textureless 3D models used in industrial applications. A self-supplied and robust markerless tracking system that combines an edge tracker, a point based tracker and a 3D particle filter has also been designed to continuously update the camera pose. Our framework incorporates an automatic path-planning module. During the offline stage, the assembly/disassembly sequence is automatically deduced from the 3D model geometry. This information is used to generate the disassembly instructions for workers.

This paper addresses the problem of camera tracking and 3D reconstruction from image sequences, i.e., the monocular SLAM problem. Traditionally, this problem is solved using non-linear minimization techniques that are very accurate but hardly used in real time. This work presents a highly parallelizable random sampling approach based on Monte Carlo simulations that fits very well on the graphics hardware. The proposed algorithm achieves the same precision as non linear optimization, getting real time performance running on commodity graphics hardware. Both accuracy and performance are evaluated using synthetic data and real video sequences captured with a hand-held camera. Moreover, results are compared with an implementation of Bundle Adjustment showing that the presented method gets similar results in much less time.

The Knowledge and Information Society development has promoted the evolution of telematic services, such as those for e-government and e-health, which aim to offer feasible solutions to social and citizens problems that will strengthen the democracy, foster citizens equal opportunities and ease their participation in the processes of the public administration and the help to the needy. Though the different telematic services are internally constituted for similar or even identical subservices, the interoperability is not possible among them and components sharing either.