Real-time underwater target detection and 3D video reconstruction for single-photon LiDAR imaging

This paper presents a statistical approach for real-time 3D image reconstruction and target detection in challenging environments. The initial step analyzes photon events to detect targets, hence selecting informative pixels for data compression. The second contribution is a reconstruction algorithm that leverages data statistics and multiscale information to produce refined depth and reflectivity images, accompanied by uncertainty maps. The algorithms are implemented to run in parallel on GPUs, facilitating the real-time processing of moving scenes with over 50 depth frames-per-second for 192×128 pixel images. Results on real underwater data show the benefit of these algorithms when compared to existing ones.