A Bayesian Approach for 3D Guided Video Super-resolution of Single-Photon LiDAR data

This paper introduces a Bayesian algorithm for the robust reconstruction and super-resolution of 3D video single-photon LiDAR data. The focus is on challenging real-world scenarios with low-resolution LiDAR data, sparse photon returns or high background noise. The proposed hierarchical Bayesian approach leverages multiscale histogram information and a high-resolution reflectivity guidance to provide high-resolution depth estimates along with corresponding uncertainty measures, aiding in better decision-making. Correlations between variables are enforced through a weighted scheme, enabling the integration of guidance from other sensors or advanced algorithms. Results on synthetic data demonstrate improved scene reconstruction in extreme conditions compared to existing methods.