Bayesian deep unfolding with graph attention for dual-peak single-photon LIDAR imaging

Single-photon Lidar is a promising 3D imaging technique, but it is challenging to deploy in real-world applications due to high noise levels and the presence of multiple surfaces per pixel. Existing statistical methods are interpretable, but limited by the assumed model. Data-driven approaches show excellent performance, but with limited interpretability, preventing their use in critical applications. In this paper, we propose an interpretable deep learning architecture with graph attention networks for the reconstruction of dual peaks per pixel in single photon Lidar. Instead of the conventional image-based representation, we represent the solution as point clouds, allowing reconstruction of more than one surface per pixel. The proposed architecture is based on a statistical Bayesian algorithm, whose iterative steps are converted into neural network layers. This approach combines the advantages of both statistical and learning-based frameworks, providing good estimates with improved network interpretability. Experimental results demonstrate the effectiveness of the proposed method.