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3D single-photon LiDAR imaging plays an important role in numerousapplications. However, long acquisition times and significant data volumespresent a challenge to LiDAR imaging. This paper proposes a task-optimizedadaptive sampling framework that enables fast acquisition and processing ofhigh-dimensional single-photon LiDAR data. Given a task of interest, theiterative sampling strategy targets the most informative regions of a scenewhich are defined as those minimizing parameter uncertainties. The task isperformed by considering a Bayesian model that is carefully built to allow fastper-pixel computations while delivering parameter estimates with quantifieduncertainties. The framework is demonstrated on multispectral 3D single-photonLiDAR imaging when considering object classification and/or target detection astasks. It is also analysed for both sequential and parallel scanning modes fordifferent detector array sizes. Results on simulated and real data show thebenefit of the proposed optimized sampling strategy when compared to fixedsampling strategies.

Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data