Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data

Abstract

Light detection and ranging (Lidar) data can be used to capture the depth and intensity profile of a 3D scene. This modality relies on constructing, for each pixel, a histogram of time delays between emitted light pulses and detected photon arrivals. In a general setting, more than one surface can be observed in a single pixel. The problem of estimating the number of surfaces, their reflectivity and position becomes very challenging in the low-photon regime (which equates to short acquisition times) or relatively high background levels (i.e., strong ambient illumination). This paper presents a new approach to 3D reconstruction using single-photon, single-wavelength Lidar data, which is capable of identifying multiple surfaces in each pixel. Adopting a Bayesian approach, the 3D structure to be recovered is modelled as a marked point process and reversible jump Markov chain Monte Carlo (RJ-MCMC) moves are proposed to sample the posterior distribution of interest. In order to promote spatial correlation between points belonging to the same surface, we propose a prior that combines an area interaction process and a Strauss process. New RJ-MCMC dilation and erosion updates are presented to achieve an efficient exploration of the configuration space. To further reduce the computational load, we adopt a multiresolution approach, processing the data from a coarse to the finest scale. The experiments performed with synthetic and real data show that the algorithm obtains better reconstructions than other recently published optimization algorithms for lower execution times.

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• Robust super-resolution depth imaging via a multi-feature fusion deep network
• Fast online 3D reconstruction of dynamic scenes from individual single-photon detection events
• A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems
• Robust 3D reconstruction of dynamic scenes from single-photon lidar using Beta-divergences
• A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar
• Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction