@inproceedings{c7ea27d4c041482ca0f94c18d3191821,
title = "On fast object detection using single-photon lidar data",
abstract = "Light detection and ranging (Lidar) systems based on single-photon detection can be used to obtain range and reflectivity information from 3D scenes with high range resolution. However, reconstructing the 3D surfaces from the raw single-photon waveforms is challenging, in particular when a limited number of photons is detected and when the ratio of spurious background detection events is large. This paper reviews a set of fast detection algorithms, which can be used to assess the presence of objects/surfaces in each waveform, allowing only the histograms where the imaged surfaces are present to be further processed. The original method we recently proposed is extended here using a multiscale approach to further reduce the computational complexity of the detection process. The proposed methods are compared to state-of-the-art 3D reconstruction methods using synthetic and real single-photon data and the results illustrate their benefits for fast and robust target detection.",
keywords = "Bayesian statistics, detection, inverse problems, Lidar, low-photon imaging and sensing",
author = "Juli{\'a}n Tachella and Yoann Altmann and Stephen McLaughlin and Jean-Yves Tourneret",
year = "2019",
month = sep,
day = "9",
doi = "10.1117/12.2527685",
language = "English",
isbn = "9781510629691",
series = "Proceedings of SPIE",
publisher = "SPIE",
editor = "{Van De Ville}, Dimitri and {Van De Ville}, Dimitri and Manos Papadakis and Lu, {Yue M.}",
booktitle = "Wavelets and Sparsity XVIII",
address = "United States",
note = "SPIE Optical Engineering + Applications 2019 ; Conference date: 11-08-2019 Through 12-08-2019",
}