Conditional Diffusion for Single-Photon LiDAR Depth Super-Resolution

Single-photon avalanche diode (SPAD) detectors offer exceptional temporal resolution and sensitivity, making them a powerful technology for depth sensing. However, reconstructing high-resolution depth maps from SPAD data is challenging due to its sparse and noisy nature, particularly in low-light or scattering conditions. This paper presents a novel SPAD-based depth map super-resolution approach that combines a SPAD's multiscale compressive representation for robustness in noisy scenarios, with high-resolution reflectivity guidance to enhance structural details. It also leverages the generative capabilities of Denoising Diffusion Probabilistic Models to quantify uncertainty. Experimental results on simulated data demonstrate the method’s effectiveness and robustness across varying noise levels and upscaling factors.