A Histogram Is Worth a Thousand Pixels: Single-Photon Metric Anchoring for Monocular Depth Estimation

Single-pixel SPAD measurements provide accurate metric range information but lack explicit spatial correspondence with image pixels, making their fusion with dense monocular depth predictions inherently ambiguous. We introduce Hist2Depth, a global-to-local framework that uses monocular depth itself to associate spatially unresolved SPAD measurements with pixel-wise depth hypotheses. Global D2 first aligns the monocular prediction with the measured depth distribution through an order-preserving metric calibration. MS-GRU then performs depth-conditioned recurrent refinement, dynamically querying SPAD evidence according to the evolving pixel-wise depth estimates and integrating it with spatial image context. On NYU-Depth-v2, Hist2Depth with AdaBins achieves 0.0649 AbsRel and 0.2750 m RMSE, and consistently improves RGB-only predictions across six monocular backbones. When transferred directly from synthetic training to captured RGB-SPAD measurements without fine-tuning, Hist2Depth also retains strong metric accuracy. Under photon thinning, performance remains nearly unchanged at a 60 s equivalent acquisition, using only 7.4% of the full photon count. These results demonstrate that global SPAD measurements can effectively guide dense metric depth reconstruction through depth-conditioned global-to-local fusion.