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

arXiv: https://arxiv.org/abs/1905.06700 · HTML: https://mlsanity.com/paper/1905.06700/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 14 of 14)

- [Active 3D Shape Reconstruction from Vision and Touch](https://mlsanity.com/paper/2107.09584/) (2021)
- [What's in a Prior? Learned Proximal Networks for Inverse Problems](https://mlsanity.com/paper/2310.14344/) (2023)
- [Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction](https://mlsanity.com/paper/2307.09555/) (2023)
- [Passive Inter-Photon Imaging](https://mlsanity.com/paper/2104.00059/) (2021)
- [Zero-Truncated Poisson Regression for Sparse Multiway Count Data Corrupted by False Zeros](https://mlsanity.com/paper/2201.10014/) (2022)
- [Resolution Limit of Single-Photon LiDAR](https://mlsanity.com/paper/2403.17719/) (2024)
- [Robust 3D Object Detection using Probabilistic Point Clouds from Single-Photon LiDARs](https://mlsanity.com/paper/2508.00169/) (2025)
- [A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems](https://mlsanity.com/paper/2201.10910/) (2022)
- [Sketched RT3D: How to reconstruct billions of photons per second](https://mlsanity.com/paper/2203.00952/) (2022)
- [TreeSketchNet: From Sketch To 3D Tree Parameters Generation](https://mlsanity.com/paper/2207.12297/) (2022)
- [Simulating single-photon detector array sensors for depth imaging](https://mlsanity.com/paper/2210.05644/) (2022)
- [Graph Attention-Driven Bayesian Deep Unrolling for Dual-Peak Single-Photon Lidar Imaging](https://mlsanity.com/paper/2504.02480/) (2025)
- [Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments](https://mlsanity.com/paper/2504.20097/) (2025)
- [Hardware-aware Coding Function Design for Compressive Single-Photon 3D Cameras](https://mlsanity.com/paper/2510.12123/) (2025)

## Abstract (arXiv)

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.