# Neural network identification of people hidden from view with a single-pixel, single-photon detector

arXiv: https://arxiv.org/abs/1709.07244 · HTML: https://mlsanity.com/paper/1709.07244/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Non-line-of-Sight Imaging via Neural Transient Fields](https://mlsanity.com/paper/2101.00373/) (2021)
- [NLOS-NeuS: Non-line-of-sight Neural Implicit Surface](https://mlsanity.com/paper/2303.12280/) (2023)
- [Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes](https://mlsanity.com/paper/2104.13807/) (2021)
- [What You Can Learn by Staring at a Blank Wall](https://mlsanity.com/paper/2108.13027/) (2021)
- [Real-time, low-cost multi-person 3D pose estimation](https://mlsanity.com/paper/2110.11414/) (2021)
- [Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction](https://mlsanity.com/paper/2203.08657/) (2022)
- [Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking](https://mlsanity.com/paper/2303.11791/) (2023)
- [Simulating single-photon detector array sensors for depth imaging](https://mlsanity.com/paper/2210.05644/) (2022)
- [Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments](https://mlsanity.com/paper/2504.20097/) (2025)

## Abstract (arXiv)

Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with an artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N=3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times