Teaser: Building quantum network with roomtemperature atomic and all-optical memories for scalable quantum information processing.Quantum memory capable of storage and retrieval of flying photons on demand is crucial for developing quantum information technologies. However, the devices needed for long-distance links are quite different from those envisioned for local processing. Here, we present the first hybrid quantum memory enabled network by demonstrating the interconnection and simultaneous operation of two types of quantum memory: an atomic-ensemble-based memory and an all-optical loop memory. The former generates and stores single atomic excitations that can then be converted to single photons; and the latter maps incoming photons in and out on demand, at room-temperature and with a broad acceptance bandwidth. Interfacing these two types of quantum memories, we observe a well-preserved quantum cross-correlation, reaching a value of 22, and a violation of the Cauchy-Schwarz inequality up to 549 standard deviations. Furthermore, we demonstrate the creation and storage of a fully operable heralded photon chain state that can achieve memory-built-in combining, swapping, splitting, tuning and chopping single photons in a chain temporally. Such a quantum network allows atomic excitations to be generated, stored, and converted to broadband photons, which are then transferred to the next node, stored, and faithfully retrieved, all at high speed and in a programmable fashion.
This paper presents a technique for monocular Structure from Motion (SFM) that reconstructs 3D world shape. The technique proposed uses optical flow for 2D pixel pair matching and Angular Bundle Ajustment (ABA) for 3D structure refinement. The proposed strategy has two main advantages. Firstly, optical flow fields provide sufficient dense correspondence of image point pairs and secondly, ABA outperforms classic BA variants, especially for the points relatively far from camera. The reconstruction results obtained in realistic scenario demonstrate the effectiveness and accuracy of the proposed algorithm.
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