Photon-efficient LiDAR provides an active remote sensing pathway for long-range, low-light three-dimensional observation, but photon-limited reconstruction remains difficult when background events dominate sparse returns. In dynamic scenes, line-of-sight motion further disperses weak-target photons across range-time bins and broadens fixed-bin accumulation. Existing methods mainly exploit histogram statistics, spatial priors, or learned representations, whereas explicit range-time migration constraints for interframe photon alignment remain insufficiently explored. We present a range-time trajectory-guided photon accumulation method for a 1064 nm InGaAs/InP $64\times 64$ GM-APD array. The method combines range-dependent background correction, multi-scale photon statistics, bounded trajectory parameter optimization using PSO, trajectory-aligned accumulation, and confidence-weighted spatial-range fusion. At -12.34 dB in simulation, SSIM, RMSE (range bins), PSNR, IoU, and FPR were 0.61, 223.22, 6.36 dB, 0.74, and 0.10%, respectively. Across 19 experimentally generated low-SBR evaluation cases derived from measured GM-APD LiDAR sequences, the corresponding mean values were $0.541\pm 0.165$, $332.93\pm 79.60$, $8.60\pm 2.49$ dB, $0.702\pm 0.061$, and $0.33\pm 0.08$%. Under the tested conditions, range-time guidance preserved spatial structure and pixel-wise range information while suppressing background responses, supporting photon-limited reconstruction in active remote sensing.

The Geiger-Mode Avalanche Photodiode (Gm-APD) LiDAR system demonstrates high-precision detection capabilities over long distances. However, the detection of occluded small objects at long distances poses significant challenges, limiting its practical application. To address this issue, we propose a multi-scale spatio-temporal object detection network (MSTOD-Net), designed to associate object information across different spatio-temporal scales for the effective detection of occluded small objects. Specifically, in the encoding stage, a dual-channel feature fusion framework is employed to process range and intensity images from consecutive time frames, facilitating the detection of occluded objects. Considering the significant differences between range and intensity images, a multi-scale context-aware (MSCA) module and a feature fusion (FF) module are incorporated to enable efficient cross-scale feature interaction and enhance small object detection. Additionally, an edge perception (EDGP) module is integrated into the network’s shallow layers to refine the edge details and enhance the information in unoccluded regions. In the decoding stage, feature maps from the encoder are upsampled and combined with multi-level fused features, and four prediction heads are employed to decode the object categories, confidence, widths and heights, and displacement offsets. The experimental results demonstrate that the MSTOD-Net achieves mAP₅₀ and mAR₅₀ scores of 96.4% and 96.9%, respectively, outperforming the state-of-the-art methods.