Single-Photon Neural Assumed-Density Filter for Dynamic Scene Reconstruction

Abstract

Single-photon LiDAR (SP-LiDAR), with its extremely high sensitivity, is well suited for imaging in photon-sparse conditions and under strong background noise. However, most existing approaches rely on histogram accumulation; in dynamic scenes, histogram accumulation mixes time-of-arrival (ToA) events across motion, jointly biasing/blurring depth and reflectivity estimates. To address these issues, we propose a Single-Photon Neural Assumed-Density Filter (SP-NADF) that operates directly on ToA events. At the system level, SP-NADF integrates photon-statistics physics with the spatio-temporal representation power of neural networks, enabling low-latency recovery of depth and reflectivity while providing actionable uncertainty estimates to assess reconstruction risk and guide robust inference. We further introduce an explicit physics-guided coupling mechanism for depth and reflectivity within this neural framework, so that the two estimates reinforce each other and improve both predictions. In addition, uncertainty-guided spatio-temporal propagation and updates help improve reconstruction stability and reliability under noise and motion. Experiments show that SP-NADF delivers higher reflectivity quality and lower depth error across noise levels and motion conditions, with clearer structures and more stable geometry, indicating strong robustness and generalization.