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<p>Three-dimensional (3D) imaging underpins applications ranging from autonomous navigation to defense and biomedicine, with single-photon avalanche diode (SPAD) light detection and ranging (LiDAR) enabling fast, long-range, and photon-efficient depth sensing. In practice, reconstruction quality is constrained by limited sensor resolution, particularly in the short- and medium-wave infrared, as well as sensor dark counts, background illumination, atmospheric effects, and motion. We introduce a unified framework for continuous-surface 3D scene representation that integrates multimodal sensing with score-based priors on latent variables. The proposed approach models scenes using a parametric continuous surface, enabling robust rendering at arbitrary spatial resolutions, even in the presence of multiple depth layers allowing imaging through camouflage. We demonstrate capabilities including data compression, targeted high-resolution rendering, and guided super-resolution of dynamic 3D videos. Validated across multiple sensing scenarios using diverse off-the-shelf priors, this framework enables compressed, high-fidelity 3D imaging in real-world environments.</p>

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Continuous-surface 3D reconstruction from kilometer-range single-photon LiDAR using score-based priors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月28日

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  • Abderrahim Halimi,
  • Jean-Yves Tourneret,
  • Aongus McCarthy,
  • Ewan Wade,
  • Rachael Tobin,
  • Jorge Garcia-Armenta,
  • Phil Soan,
  • Gerald S. Buller

摘要

Three-dimensional (3D) imaging underpins applications ranging from autonomous navigation to defense and biomedicine, with single-photon avalanche diode (SPAD) light detection and ranging (LiDAR) enabling fast, long-range, and photon-efficient depth sensing. In practice, reconstruction quality is constrained by limited sensor resolution, particularly in the short- and medium-wave infrared, as well as sensor dark counts, background illumination, atmospheric effects, and motion. We introduce a unified framework for continuous-surface 3D scene representation that integrates multimodal sensing with score-based priors on latent variables. The proposed approach models scenes using a parametric continuous surface, enabling robust rendering at arbitrary spatial resolutions, even in the presence of multiple depth layers allowing imaging through camouflage. We demonstrate capabilities including data compression, targeted high-resolution rendering, and guided super-resolution of dynamic 3D videos. Validated across multiple sensing scenarios using diverse off-the-shelf priors, this framework enables compressed, high-fidelity 3D imaging in real-world environments.