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

TL;DR

This paper introduces a hierarchical Bayesian method for robustly reconstructing multispectral single-photon Lidar data in noisy and challenging environments, leveraging multi-scale information and guide data for improved depth and reflectivity estimation.

Contribution

It proposes a novel Bayesian reconstruction algorithm that integrates multi-scale analysis and guide information, enhancing robustness and accuracy in difficult imaging conditions.

Findings

Validated on synthetic and real data with competitive results

Achieves high-quality depth and reflectivity estimates

Maintains computational efficiency compared to state-of-the-art methods

Abstract

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.