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3D Lidar imaging can be a challenging modality when using multiplewavelengths, or when imaging in high noise environments (e.g., imaging throughobscurants). This paper presents a hierarchical Bayesian algorithm for therobust reconstruction of multispectral single-photon Lidar data in suchenvironments. The algorithm exploits multi-scale information to provide robustdepth and reflectivity estimates together with their uncertainties to help withdecision making. The proposed weight-based strategy allows the use of availableguide information that can be obtained by using state-of-the-art learning basedalgorithms. The proposed Bayesian model and its estimation algorithm arevalidated on both synthetic and real images showing competitive resultsregarding the quality of the inferences and the computational complexity whencompared to the state-of-the-art algorithms.

Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging