Optics InfoBase is the Optical Society's online library for flagship journals, partnered and copublished journals, and recent proceedings from OSA conferences.

We describe a scanning time-of-flight system which uses the time-correlated single-photon counting technique to produce three-dimensional depth images of distant, noncooperative surfaces when these targets are illuminated by a kHz to MHz repetition rate pulsed laser source. The data for the scene are acquired using a scanning optical system and an individual single-photon detector. Depth images have been successfully acquired with centimeter $xyz$ resolution, in daylight conditions, for low-signature targets in field trials at distances of up to $325\mathrm{m}$ using an output illumination with an average optical power of less than $50\mathrm{\mu W}$.

Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. Here we show a new computational framework for real-time three-dimensional (3D) scene reconstruction from single-photon data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad daylight from distances up to 320 metres. The proposed method can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes. This enables robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications.

Optics InfoBase is the Optical Society's online library for flagship journals, partnered and copublished journals, and recent proceedings from OSA conferences.

A depth imaging system, based on the time-of-flight approach and the time-correlated single-photon counting (TCSPC) technique, was investigated for use in highly scattering underwater environments. The system comprised a pulsed supercontinuum laser source, a monostatic scanning transceiver, with a silicon single-photon avalanche diode (SPAD) used for detection of the returned optical signal. Depth images were acquired in the laboratory at stand-off distances of up to 8 attenuation lengths, using per-pixel acquisition times in the range 0.5 to 100 ms, at average optical powers in the range 0.8 nW to 950 μW. In parallel, a LiDAR model was developed and validated using experimental data. The model can be used to estimate the performance of the system under a variety of scattering conditions and system parameters.

This paper presents a new Bayesian model and algorithm used for depth and reflectivity profiling using full waveforms from the time-correlated single-photon counting measurement in the limit of very low photon counts. The proposed model represents each Lidar waveform as a combination of a known impulse response, weighted by the target reflectivity, and an unknown constant background, corrupted by Poisson noise. Prior knowledge about the problem is embedded through prior distributions that account for the different parameter constraints and their spatial correlation among the image pixels. In particular, a gamma Markov random field (MRF) is used to model the joint distribution of the target reflectivity, and a second MRF is used to model the distribution of the target depth, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to perform Bayesian inference. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a series of experiments using real data.