Real-Time Reconstruction of 3D Videos From Single-Photon LiDaR Data in the Presence of Obscurants

Single-photon methods are emerging as a key approach to 3D imaging. This paper introduces a two step statistical based approach for real-time image reconstruction applicable to a transmission medium with extreme light scattering conditions. The first step is an optional target detection method to select informative pixels which have photons reflected from the target, hence allowing data compression. The second is a reconstruction algorithm that exploits data statistics and multiscale information to deliver clean depth and reflectivity images together with associated uncertainty maps. Both methods involve independent operations that are implemented in parallel on graphics processing units (GPUs), which enables real-time data processing of moving scenes at more than 50 depth frames per second for an image of
$128 \times 128$
pixels. Comparisons with state-of-the-art algorithms on simulated and real underwater data demonstrate the benefit of the proposed framework for target detection, and for fast and robust depth estimation at multiple frames per second.