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Our goal is to recover a complete 3D model from a depth image of an object. Although we only see a small portion of an object from a single viewpoint, we can accurately predict the entire shape. This ability to infer complete 3D shape from a single view is important for grasping as we often reach around an object to grasp its unseen surfaces. Likewise,(More)
We analyze a decentralized random-walk based algorithm for data collection at the sink in a multi-hop sensor network. Our algorithm, Metropolis-Collect, which involves data packets being passed to random neighbors in the network according to a simple metropolis random-walk mechanism, requires no configuration and incurs no routing overhead. To analyze this(More)
We propose an approach for 3D reconstruction and segmentation of a single object placed on a flat surface from an input video. Our approach is to perform dense depth map estimation for multiple views using a proposed objective function that preserves detail. The resulting depth maps are then fused using a proposed implicit surface function that is robust to(More)
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