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

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.

Automated summary

This paper introduces a hierarchical Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data in challenging environments, providing robust depth and reflectivity estimates using multi-scale information to assist decision making. The weight-based strategy allows the utilization of guide information obtained by state-of-the-art learning based algorithms, with validation showing competitive results in terms of quality of inferences and computational complexity compared to existing algorithms.