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

## TL;DR

This paper introduces a fast, real-time 3D scene reconstruction method from single-photon lidar data, enabling practical outdoor imaging in daylight with high accuracy and speed.

## Contribution

It presents a novel computational framework combining statistical models and scalable point cloud denoisers for real-time 3D reconstruction from single-photon lidar data.

## Key findings

- Achieves 3D reconstruction in about 20 ms per frame.
- Handles multiple surfaces per pixel and cluttered scenes.
- Operates effectively at 320 meters in daylight conditions.

## 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.