Abstract
To address the bottlenecks of low optical efficiency and uneven illumination inherent to single-photon array LiDAR systems, this paper proposes a joint imaging framework combining freeform light field shaping via an orthogonal bidirectional iterative algorithm and total-variation (TV) regularized image reconstruction. The core optical contribution lies in a freeform lens design strategy based on orthogonal bidirectional iteration, which establishes a precise energy mapping between incident Gaussian beams and uniform lattice light fields matching GM-APD pixel layouts. This refractive reshaping converts raw Gaussian radiation into flat-top spot arrays with homogenized energy distribution. Numerical simulations verify that the proposed freeform modulation achieves an optical energy utilization of 89.1% and a light uniformity of 96.5%, fundamentally resolving the tradeoff between central pixel saturation and peripheral detection blind zones in array receivers. For photon-sparse, noise-contaminated raw echo data, a reconstruction workflow integrating maximum likelihood estimation (MLE) and conventional TV regularization is adopted to suppress stochastic noise while preserving sharp object geometric edges. Nighttime field experiments targeting a scene at 22.4 m demonstrate that the mean absolute error (MAE) of depth reconstruction is reduced from 2.2373 m under unoptimized flood illumination to 0.3176 m with our freeform lattice illumination. The presented scheme fulfills the large field-of-view (FOV) requirement of array-based single-photon detection and yields remarkable imaging improvements under constraints of limited laser output power and compact optical packaging. This work delivers a feasible optical route for high-efficiency, long-range single-photon 3D imaging LiDAR.