  3D single-photon LiDAR imaging plays an important role in numerous
applications. However, long acquisition times and significant data volumes
present a challenge to LiDAR imaging. This paper proposes a task-optimized
adaptive sampling framework that enables fast acquisition and processing of
high-dimensional single-photon LiDAR data. Given a task of interest, the
iterative sampling strategy targets the most informative regions of a scene
which are defined as those minimizing parameter uncertainties. The task is
performed by considering a Bayesian model that is carefully built to allow fast
per-pixel computations while delivering parameter estimates with quantified
uncertainties. The framework is demonstrated on multispectral 3D single-photon
LiDAR imaging when considering object classification and/or target detection as
tasks. It is also analysed for both sequential and parallel scanning modes for
different detector array sizes. Results on simulated and real data show the
benefit of the proposed optimized sampling strategy when compared to fixed
sampling strategies.
