494 research outputs found
Frequency-modulated continuous-wave LiDAR compressive depth-mapping
We present an inexpensive architecture for converting a frequency-modulated
continuous-wave LiDAR system into a compressive-sensing based depth-mapping
camera. Instead of raster scanning to obtain depth-maps, compressive sensing is
used to significantly reduce the number of measurements. Ideally, our approach
requires two difference detectors. % but can operate with only one at the cost
of doubling the number of measurments. Due to the large flux entering the
detectors, the signal amplification from heterodyne detection, and the effects
of background subtraction from compressive sensing, the system can obtain
higher signal-to-noise ratios over detector-array based schemes while scanning
a scene faster than is possible through raster-scanning. %Moreover, we show how
a single total-variation minimization and two fast least-squares minimizations,
instead of a single complex nonlinear minimization, can efficiently recover
high-resolution depth-maps with minimal computational overhead. Moreover, by
efficiently storing only data points from measurements of an
pixel scene, we can easily extract depths by solving only two linear equations
with efficient convex-optimization methods
Frequency-Modulated Continuous-Wave LiDAR Compressive Depth-Mapping
We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. Moreover, by efficiently storing only 2m data points from m \u3c n measurements of an n pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods
Frequency Modulated Continuous Wave Compressive Depth Mapping
We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. Moreover, by efficiently storing only 2m data points from m \u3c n measurements of an n pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods
Recent advances in transient imaging: A computer graphics and vision perspective
Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation
Recent advances in transient imaging: A computer graphics and vision perspective
Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation
Advanced Intensity-Modulation Continuous-Wave Lidar Techniques for ASCENDS O2 Column Measurements
Global atmospheric carbon dioxide (CO2) measurements for the NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) space mission are critical for improving our understanding of global CO2 sources and sinks. Advanced Intensity- Modulated Continuous-Wave (IM-CW) lidar techniques are investigated as a means of facilitating CO2 measurements from space to meet the ASCENDS measurement requirements. In recent numerical, laboratory and flight experiments we have successfully used the Binary Phase Shift Keying (BPSK) modulation technique to uniquely discriminate surface lidar returns from intermediate aerosol and cloud contamination. We demonstrate the utility of BPSK to eliminate sidelobes in the range profile as a means of making Integrated Path Differential Absorption (IPDA) column CO2 measurements in the presence of optically thin clouds, thereby eliminating the need to correct for sidelobe bias errors caused by the clouds. Furthermore, high accuracy and precision ranging to the surface as well as to the top of intermediate cloud layers, which is a requirement for the inversion of column CO2 number density measurements to column CO2 mixing ratios, has been demonstrated using new hyperfine interpolation techniques that takes advantage of the periodicity of the modulation waveforms. This approach works well for both BPSK and linear swept-frequency modulation techniques. The BPSK technique under investigation has excellent auto-correlation properties while possessing a finite bandwidth. A comparison of BPSK and linear swept-frequency is also discussed in this paper. These results are extended to include Richardson-Lucy deconvolution techniques to extend the resolution of the lidar beyond that implied by limit of the bandwidth of the modulation, where it is shown useful for making tree canopy measurements
Single-pixel, single-photon three-dimensional imaging
The 3D recovery of a scene is a crucial task with many real-life applications such as self-driving vehicles, X-ray tomography and virtual reality. The recent development of time-resolving detectors sensible to single photons allowed the recovery of the 3D information at high frame rate with unprecedented capabilities. Combined with a timing system, single-photon sensitive detectors
allow the 3D image recovery by measuring the Time-of-Flight (ToF) of the photons scattered back by the scene with a millimetre depth resolution.
Current ToF 3D imaging techniques rely on scanning detection systems or multi-pixel sensor.
Here, we discuss an approach to simplify the hardware complexity of the current 3D imaging ToF techniques using a single-pixel, single-photon sensitive detector and computational imaging algorithms. The 3D imaging approaches discussed in this thesis do not require mechanical moving
parts as in standard Lidar systems. The single-pixel detector allows to reduce the pixel complexity to a single unit and offers several advantages in terms of size, flexibility, wavelength range and cost. The experimental results demonstrate the 3D image recovery of hidden scenes with a subsecond
acquisition time, allowing also non-line-of-sight scenes 3D recovery in real-time. We also introduce the concept of intelligent Lidar, a 3D imaging paradigm based uniquely on the temporal trace of the return photons and a data-driven 3D retrieval algorithm
Imaging With Nature: Compressive Imaging Using a Multiply Scattering Medium
The recent theory of compressive sensing leverages upon the structure of
signals to acquire them with much fewer measurements than was previously
thought necessary, and certainly well below the traditional Nyquist-Shannon
sampling rate. However, most implementations developed to take advantage of
this framework revolve around controlling the measurements with carefully
engineered material or acquisition sequences. Instead, we use the natural
randomness of wave propagation through multiply scattering media as an optimal
and instantaneous compressive imaging mechanism. Waves reflected from an object
are detected after propagation through a well-characterized complex medium.
Each local measurement thus contains global information about the object,
yielding a purely analog compressive sensing method. We experimentally
demonstrate the effectiveness of the proposed approach for optical imaging by
using a 300-micrometer thick layer of white paint as the compressive imaging
device. Scattering media are thus promising candidates for designing efficient
and compact compressive imagers.Comment: 17 pages, 8 figure
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