Abstract. Capturing the motion of two hands interacting with an object is a very challenging task due to the large number of degrees of freedom, self-occlusions, and similarity between the fingers, even in the case of multiple cameras observing the scene. In this paper we propose to use discriminatively learned salient points on the fingers and to estimate the finger-salient point associations simultaneously with the estimation of the hand pose. We introduce a differentiable objective function that also takes edges, optical flow and collisions into account. Our qualitative and quantitative evaluations show that the proposed approach achieves very accurate results for several challenging sequences containing hands and objects in action.

In this paper, we propose an efficient technique to detect changes in the geometry of an urban environment using some images observing its current state. The proposed method can be used to significantly optimize the process of updating the 3D model of a city changing over time, by restricting this process to only those areas where changes are detected. With this application in mind, we designed our algorithm to specifically detect only structural changes in the environment, ignoring any changes in its appearance, and ignoring also all the changes which are not relevant for update purposes, such as cars, people etc. As a by-product, the algorithm also provides a coarse geometry of the detected changes. The performance of the proposed method was tested on four different kinds of urban environments and compared with two alternative techniques.

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The availability of geolocated panoramic images of urban environments has been increasing in the recent past thanks to services like Google StreetView, Microsoft StreetSide, and Navteq. Despite the fact that their primary application is in street navigation, these images can be used, along with cadastral information, for city planning, real-estate evaluation and tracking of changes in an urban environment. The geolocation information, provided with these images, is however not accurate enough for such applications: this inaccuracy can be observed in both the position and orientation of the camera, due to noise introduced during the acquisition. We propose a method to refine the calibration of these images leveraging cadastral 3D information, typically available in urban scenarios. We evaluated the algorithm on a city scale dataset, spanning commercial and residential areas, as well as the countryside.

Dynamic scene modeling is a challenging problem in computer vision. Many techniques have been developed in the past to address such a problem but most of them focus on achieving accurate reconstructions in controlled environments, where the background and the lighting are known and the cameras are fixed and calibrated. Recent approaches have relaxed these requirements by applying these techniques to outdoor scenarios. The problem however becomes even harder when the cameras are allowed to move during the recording since no background color model can be easily inferred. In this paper we propose a new approach to model dynamic scenes captured in outdoor environments with moving cameras. A probabilistic framework is proposed to deal with such a scenario and to provide a volumetric reconstruction of all the dynamic elements of the scene. The proposed algorithm was tested on a publicly available dataset filmed outdoors with six moving cameras. A quantitative evaluation of the method was also performed on synthetic data. The obtained results demonstrated the effectiveness of the approach considering the complexity of the problem.