Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers

Julián Tachella, Yoann Altmann (), Nicolas Mellado, Aongus McCarthy, Rachael Tobin, Gerald S. Buller, Jean-Yves Tourneret and Stephen McLaughlin
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Julián Tachella: Heriot-Watt University
Yoann Altmann: Heriot-Watt University
Nicolas Mellado: University of Toulouse
Aongus McCarthy: Heriot-Watt University
Rachael Tobin: Heriot-Watt University
Gerald S. Buller: Heriot-Watt University
Jean-Yves Tourneret: University of Toulouse
Stephen McLaughlin: Heriot-Watt University

Nature Communications, 2019, vol. 10, issue 1, 1-6

Abstract: Abstract Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. Here we show a new computational framework for real-time three-dimensional (3D) scene reconstruction from single-photon data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad daylight from distances up to 320 metres. The proposed method can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes. This enables robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications.

Date: 2019
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DOI: 10.1038/s41467-019-12943-7

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