8,573 research outputs found

    ESL: Event-based Structured Light

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    Event cameras are bio-inspired sensors providing significant advantages over standard cameras such as low latency, high temporal resolution, and high dynamic range. We propose a novel structured-light system using an event camera to tackle the problem of accurate and high-speed depth sensing. Our setup consists of an event camera and a laser-point projector that uniformly illuminates the scene in a raster scanning pattern during 16 ms. Previous methods match events independently of each other, and so they deliver noisy depth estimates at high scanning speeds in the presence of signal latency and jitter. In contrast, we optimize an energy function designed to exploit event correlations, called spatio-temporal consistency. The resulting method is robust to event jitter and therefore performs better at higher scanning speeds. Experiments demonstrate that our method can deal with high-speed motion and outperform state-of-the-art 3D reconstruction methods based on event cameras, reducing the RMSE by 83% on average, for the same acquisition time. Code and dataset are available at http://rpg.ifi.uzh.ch/esl/

    ESL: Event-based Structured Light

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    Event cameras are bio-inspired sensors providing significant advantages over standard cameras such as low latency, high temporal resolution, and high dynamic range. We propose a novel structured-light system using an event camera to tackle the problem of accurate and high-speed depth sensing. Our setup consists of an event camera and a laser-point projector that uniformly illuminates the scene in a raster scanning pattern during 16 ms. Previous methods match events independently of each other, and so they deliver noisy depth estimates at high scanning speeds in the presence of signal latency and jitter. In contrast, we optimize an energy function designed to exploit event correlations, called spatio-temporal consistency. The resulting method is robust to event jitter and therefore performs better at higher scanning speeds. Experiments demonstrate that our method can deal with high-speed motion and outperform state-of-the-art 3D reconstruction methods based on event cameras, reducing the RMSE by 83% on average, for the same acquisition time. Code and dataset are available at http://rpg.ifi.uzh.ch/esl/

    Deep learning optimized single-pixel LiDAR

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    Interest in autonomous transport has led to a demand for 3D imaging technologies capable of resolving fine details at long range. Light detection and ranging (LiDAR) systems have become a key technology in this area, with depth information typically gained through time-of-flight photon-counting measurements of a scanned laser spot. Single-pixel imaging methods offer an alternative approach to spot-scanning, which allows a choice of sampling basis. In this work, we present a prototype LiDAR system, which compressively samples the scene using a deep learning optimized sampling basis and reconstruction algorithms. We demonstrate that this approach improves scene reconstruction quality compared to an orthogonal sampling method, with reflectivity and depth accuracy improvements of 57% and 16%, respectively, for one frame per second acquisition rates. This method may pave the way for improved scan-free LiDAR systems for driverless cars and for fully optimized sampling to decision-making pipelines

    Single particle trajectories reveal active endoplasmic reticulum luminal flow

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    The endoplasmic reticulum (ER), a network of membranous sheets and pipes, supports functions encompassing biogenesis of secretory proteins and delivery of functional solutes throughout the cell[1, 2]. Molecular mobility through the ER network enables these functionalities, but diffusion alone is not sufficient to explain luminal transport across supramicrometre distances. Understanding the ER structure–function relationship is critical in light of mutations in ER morphology-regulating proteins that give rise to neurodegenerative disorders[3, 4]. Here, super-resolution microscopy and analysis of single particle trajectories of ER luminal proteins revealed that the topological organization of the ER correlates with distinct trafficking modes of its luminal content: with a dominant diffusive component in tubular junctions and a fast flow component in tubules. Particle trajectory orientations resolved over time revealed an alternating current of the ER contents, while fast ER super-resolution identified energy-dependent tubule contraction events at specific points as a plausible mechanism for generating active ER luminal flow. The discovery of active flow in the ER has implications for timely ER content distribution throughout the cell, particularly important for cells with extensive ER-containing projections such as neurons.Wellcome Trust - 3-3249/Z/16/Z and 089703/Z/09/Z [Kaminski] UK Demential Research Institute [Avezov] Wellcome Trust - 200848/Z/16/Z, WT: UNS18966 [Ron] FRM Team Research Grant [Holcman] Engineering and Physical Sciences Research Council (EPSRC) - EP/L015889/1 and EP/H018301/1 [Kaminski] Medical Research Council (MRC) - MR/K015850/1 and MR/K02292X/1 [Kaminski

    Concepts in Light Microscopy of Viruses

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    Viruses threaten humans, livestock, and plants, and are difficult to combat. Imaging of viruses by light microscopy is key to uncover the nature of known and emerging viruses in the quest for finding new ways to treat viral disease and deepening the understanding of virus–host interactions. Here, we provide an overview of recent technology for imaging cells and viruses by light microscopy, in particular fluorescence microscopy in static and live-cell modes. The review lays out guidelines for how novel fluorescent chemical probes and proteins can be used in light microscopy to illuminate cells, and how they can be used to study virus infections. We discuss advantages and opportunities of confocal and multi-photon microscopy, selective plane illumination microscopy, and super-resolution microscopy. We emphasize the prevalent concepts in image processing and data analyses, and provide an outlook into label-free digital holographic microscopy for virus research

    Single-photon detection techniques for underwater imaging

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    This Thesis investigates the potential of a single-photon depth profiling system for imaging in highly scattering underwater environments. This scanning system measured depth using the time-of-flight and the time-correlated single-photon counting (TCSPC) technique. The system comprised a pulsed laser source, a monostatic scanning transceiver, with a silicon single-photon avalanche diode (SPAD) used for detection of the returned optical signal. Spectral transmittance measurements were performed on a number of different water samples in order to characterize the water types used in the experiments. This identified an optimum operational wavelength for each environment selected, which was in the wavelength region of 525 - 690 nm. Then, depth profiles measurements were performed in different scattering conditions, demonstrating high-resolution image re-construction for targets placed at stand-off distances up to nine attenuation lengths, using average optical power in the sub-milliwatt range. Depth and spatial resolution were investigated in several environments, demonstrating a depth resolution in the range of 500 μm to a few millimetres depending on the attenuation level of the medium. The angular resolution of the system was approximately 60 μrad in water with different levels of attenuation, illustrating that the narrow field of view helped preserve spatial resolution in the presence of high levels of forward scattering. Bespoke algorithms were developed for image reconstruction in order to recover depth, intensity and reflectivity information, and to investigate shorter acquisition times, illustrating the practicality of the approach for rapid frame rates. In addition, advanced signal processing approaches were used to investigate the potential of multispectral single-photon depth imaging in target discrimination and recognition, in free-space and underwater environments. Finally, a LiDAR model was developed and validated using experimental data. The model was used to estimate the performance of the system under a variety of scattering conditions and system parameters
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