  3D Lidar imaging can be a challenging modality when using multiple
wavelengths, or when imaging in high noise environments (e.g., imaging through
obscurants). This paper presents a hierarchical Bayesian algorithm for the
robust reconstruction of multispectral single-photon Lidar data in such
environments. The algorithm exploits multi-scale information to provide robust
depth and reflectivity estimates together with their uncertainties to help with
decision making. The proposed weight-based strategy allows the use of available
guide information that can be obtained by using state-of-the-art learning based
algorithms. The proposed Bayesian model and its estimation algorithm are
validated on both synthetic and real images showing competitive results
regarding the quality of the inferences and the computational complexity when
compared to the state-of-the-art algorithms.
