  In this paper, we present a new algorithm for fast, online 3D reconstruction
of dynamic scenes using times of arrival of photons recorded by single-photon
detector arrays. One of the main challenges in 3D imaging using single-photon
lidar in practical applications is the presence of strong ambient illumination
which corrupts the data and can jeopardize the detection of peaks/surface in
the signals. This background noise not only complicates the observation model
classically used for 3D reconstruction but also the estimation procedure which
requires iterative methods. In this work, we consider a new similarity measure
for robust depth estimation, which allows us to use a simple observation model
and a non-iterative estimation procedure while being robust to
mis-specification of the background illumination model. This choice leads to a
computationally attractive depth estimation procedure without significant
degradation of the reconstruction performance. This new depth estimation
procedure is coupled with a spatio-temporal model to capture the natural
correlation between neighboring pixels and successive frames for dynamic scene
analysis. The resulting online inference process is scalable and well suited
for parallel implementation. The benefits of the proposed method are
demonstrated through a series of experiments conducted with simulated and real
single-photon lidar videos, allowing the analysis of dynamic scenes at 325 m
observed under extreme ambient illumination conditions.
