Figure 1: Given single-view scans by the Kinect system, containing highly noisy and incomplete 3D scans (upper left) and corresponding RGB images (lower left), our approach is able to faithfully recover their underlying structures (yellow) by assembling suitable parts (red) in the repository models (blue). AbstractThis paper presents a technique that allows quick conversion of acquired low-quality data from consumer-level scanning devices to high-quality 3D models with labeled semantic parts and meanwhile their assembly reasonably close to the underlying geometry. This is achieved by a novel structure recovery approach that is essentially local to global and bottom up, enabling the creation of new structures by assembling existing labeled parts with respect to the acquired data. We demonstrate that using only a small-scale shape repository, our part assembly approach is able to faithfully recover a variety of high-level structures from only a single-view scan of man-made objects acquired by the Kinect system, containing a highly noisy, incomplete 3D point cloud and a corresponding RGB image. Links:DL PDF WEB

SUMMARYEffective prediction of bus arrival times is important to advanced traveler information systems (ATIS). Here a hybrid model, based on support vector machine (SVM) and Kalman filtering technique, is presented to predict bus arrival times. In the model, the SVM model predicts the baseline travel times on the basic of historical trips occurring data at given time-of-day, weather conditions, route segment, the travel times on the current segment, and the latest travel times on the predicted segment; the Kalman filtering-based dynamic algorithm uses the latest bus arrival information, together with estimated baseline travel times, to predict arrival times at the next point. The predicted bus arrival times are examined by data of bus no. 7 in a satellite town of Dalian in China. Results show that the hybrid model proposed in this paper is feasible and applicable in bus arrival time forecasting area, and generally provides better performance than artificial neural network (ANN)-based methods.

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The increasing popularity of cloud storage services attracts large amounts of companies to store their data in cloud instead of building their own infrastructures. With large amounts of data stored in the cloud, it is expected to provide high availability and fine global access experiences. However, there are still major concerns of the availability of major cloud services, especially in a sparsely connected global network with complicated issues. In this paper, we introduce μLibCloud, a system based on Apache libCloud, aiming to improve the availability and global access experience of clouds, and to tolerate provider failures and outages. μLibCloud works as a library at client side, transparently spreading and collecting data smartly to/from different cloud providers through erasure code. In evaluation, we deployed the system into 7 major cloud providers and run a global benchmarks from 9 locations around the world. The results were compared to the original clouds and a content delivery network. We observed that μLibCloud achieved a higher and more uniformed read availability in most cases, with reasonable estimated extra costs. For example, the read latency of some original providers could be reduced by 50%-70% at different locations.