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Joint Depth and Reflectivity Estimation using Single-Photon LiDAR

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arxiv 2505.13250 v1 pith:VRPZNTNG submitted 2025-05-19 cs.CV

classification cs.CV
keywords depthreflectivitysp-lidarestimationjointmethodreconstructionscenes
verification ladder T0 review T1 audit T2 compute T3 formal
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Single-Photon Light Detection and Ranging (SP-LiDAR is emerging as a leading technology for long-range, high-precision 3D vision tasks. In SP-LiDAR, timestamps encode two complementary pieces of information: pulse travel time (depth) and the number of photons reflected by the object (reflectivity). Existing SP-LiDAR reconstruction methods typically recover depth and reflectivity separately or sequentially use one modality to estimate the other. Moreover, the conventional 3D histogram construction is effective mainly for slow-moving or stationary scenes. In dynamic scenes, however, it is more efficient and effective to directly process the timestamps. In this paper, we introduce an estimation method to simultaneously recover both depth and reflectivity in fast-moving scenes. We offer two contributions: (1) A theoretical analysis demonstrating the mutual correlation between depth and reflectivity and the conditions under which joint estimation becomes beneficial. (2) A novel reconstruction method, "SPLiDER", which exploits the shared information to enhance signal recovery. On both synthetic and real SP-LiDAR data, our method outperforms existing approaches, achieving superior joint reconstruction quality.

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  1. Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping

    eess.SP 2025-05 conditional novelty 5.0 of 10

    An autoencoder maps LiDAR flux functions to dead-time-distorted registration PDFs, accelerating high-flux single-photon LiDAR simulation by orders of magnitude while matching conventional simulation histograms.

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