Fig. 1. Schematic diagram of the algorithm. (a) Single-photon LiDAR data acquired by laser source with multiple repetition rates. (b) Image captured by camera. (c) Intensity image of (b). (d) Absolute distance image. (e) Horizontal, vertical, and diagonal gradient images from the camera image. (f) High-resolution depth image without range ambiguity.

Fig. 2. Simulation results. (a) Ground truth. (b) High-resolution camera image. (c) The simulation results by different methods under various PPP and SBR. From top to bottom, each row corresponds to PPP ∼1 with SBR ∼0.1, PPP ∼10 with SBR ∼0.01, and PPP ∼10 with SBR ∼0.1, respectively. From left to right, each column shows the results reconstructed by Snyder et al. and Dai et al., proposed without and with upsampling, respectively.

Fig. 3. The RMSE in simulations with different PPP and SBR levels. (a) For PPP ∼1 with SBR ∼0.01, 0.05, and 0.1, the RMSE results are calculated by the methods of Dai et al., proposed with and without upsampling. (b) For SBR ∼0.1 with PPP ∼0.5, 1, 5, and 10, the RMSE results are calculated by the methods of Dai et al., proposed without and with upsampling.

Fig. 4. The layout of the system. (a) Conventional high-resolution camera. (b) Single-photon LiDAR. (c) Data processing system.

Fig. 5. The experimental results. (a) The target’s location on the map. (b) Photograph of our system. (c) High-resolution camera image of target. (d), (e) The results using our proposed method without and with upsampling. (f), (g) Closeup views of the building details in depth reconstructions [area highlighted by green rectangle in (c)]. (h), (i) 3D profiles of the eaves details in depth reconstructions [highlighted by blue rectangle in (c)].