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

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