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

Abderrahim Halimi1, Jean-Yves Tourneret2, Aongus McCarthy3

  • 1School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK. a.halimi@hw.ac.uk.

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|June 28, 2026
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Summary

This study introduces a new 3D imaging framework using continuous surfaces and multimodal sensing. It enhances depth sensing with single-photon avalanche diode (SPAD) LiDAR for high-fidelity 3D scene reconstruction.

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Area of Science:

  • Optics and Photonics
  • Computer Vision
  • Signal Processing

Background:

  • Three-dimensional (3D) imaging is crucial for autonomous navigation, defense, and biomedicine.
  • Single-photon avalanche diode (SPAD) Light Detection and Ranging (LiDAR) offers fast, long-range, and photon-efficient depth sensing.
  • Reconstruction quality is limited by sensor resolution, noise, and environmental factors.

Purpose of the Study:

  • To develop a unified framework for continuous-surface 3D scene representation.
  • To integrate multimodal sensing with score-based priors for robust 3D imaging.
  • To overcome limitations in current 3D imaging technologies.

Main Methods:

  • Modeling scenes using a parametric continuous surface representation.
  • Integrating multimodal sensing data with score-based priors on latent variables.
  • Enabling robust rendering at arbitrary spatial resolutions, including through camouflage.

Main Results:

  • Demonstrated capabilities in data compression and targeted high-resolution rendering.
  • Achieved guided super-resolution of dynamic 3D videos.
  • Enabled compressed, high-fidelity 3D imaging in diverse real-world sensing scenarios.

Conclusions:

  • The proposed framework offers a robust solution for 3D scene representation and reconstruction.
  • It effectively integrates multimodal sensing and advanced priors to enhance imaging quality.
  • This approach advances the capabilities of 3D imaging for various applications.