# Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path

arXiv: https://arxiv.org/abs/1912.06727 · HTML: https://mlsanity.com/paper/1912.06727/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [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)
- [Enhancing Autonomous Navigation by Imaging Hidden Objects using Single-Photon LiDAR](https://mlsanity.com/paper/2410.03555/) (2024)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [3D Scene Inference from Transient Histograms](https://mlsanity.com/paper/2211.05094/) (2022)
- [Non-Line-of-Sight Tracking and Mapping with an Active Corner Camera](https://mlsanity.com/paper/2208.01702/) (2022)
- [Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking](https://mlsanity.com/paper/2303.11791/) (2023)
- [Synthetic Wavelength Imaging -- Utilizing Spectral Correlations for High-Precision Time-of-Flight Sensing](https://mlsanity.com/paper/2209.04941/) (2022)
- [Onsite Non-Line-of-Sight Imaging via Online Calibrations](https://mlsanity.com/paper/2112.14555/) (2021)
- [TransiT: Transient Transformer for Non-line-of-sight Videography](https://mlsanity.com/paper/2503.11328/) (2025)
- [Imaging Hidden Objects with Consumer LiDAR via Motion Induced Sampling](https://mlsanity.com/paper/2605.17865/) (2026)
- [Non-line-of-sight imaging with arbitrary relay surface geometries via 3D Gaussian Transient Rendering](https://mlsanity.com/paper/2606.21270/) (2026)

## Abstract (arXiv)

Non-line-of-sight (NLOS) imaging and tracking is an emerging technology that allows the shape or position of objects around corners or behind diffusers to be recovered from transient, time-of-flight measurements. However, existing NLOS approaches require the imaging system to scan a large area on a visible surface, where the indirect light paths of hidden objects are sampled. In many applications, such as robotic vision or autonomous driving, optical access to a large scanning area may not be available, which severely limits the practicality of existing NLOS techniques. Here, we propose a new approach, dubbed keyhole imaging, that captures a sequence of transient measurements along a single optical path, for example, through a keyhole. Assuming that the hidden object of interest moves during the acquisition time, we effectively capture a series of time-resolved projections of the object's shape from unknown viewpoints. We derive inverse methods based on expectation-maximization to recover the object's shape and location using these measurements. Then, with the help of long exposure times and retroreflective tape, we demonstrate successful experimental results with a prototype keyhole imaging system.