# Non-Line-of-Sight Tracking and Mapping with an Active Corner Camera

arXiv: https://arxiv.org/abs/2208.01702 · HTML: https://mlsanity.com/paper/2208.01702/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [Non-line-of-sight Imaging](https://mlsanity.com/paper/2005.08026/) (2020)
- [A megapixel time-gated SPAD image sensor for 2D and 3D imaging applications](https://mlsanity.com/paper/1912.12910/) (2019)
- [Fast Back-Projection for Non-Line of Sight Reconstruction](https://mlsanity.com/paper/1703.02016/) (2017)
- [Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization](https://mlsanity.com/paper/1912.02314/) (2019)
- [Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path](https://mlsanity.com/paper/1912.06727/) (2019)
- [Seeing Around Corners with Edge-Resolved Transient Imaging](https://mlsanity.com/paper/2002.07118/) (2020)
- [Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder](https://mlsanity.com/paper/2006.09241/) (2020)
- [Non-line-of-sight tracking of people at long range](https://mlsanity.com/paper/1703.02124/) (2017)
- [Dead Time Compensation for High-Flux Ranging](https://mlsanity.com/paper/1810.11145/) (2018)
- [Exploiting Occlusion in Non-Line-of-Sight Active Imaging](https://mlsanity.com/paper/1711.06297/) (2017)

## Built on by (most cited first, 2 of 2)

- [Neural Inverse Rendering from Propagating Light](https://mlsanity.com/paper/2506.05347/) (2025)
- [Soft Shadow Diffusion (SSD): Physics-inspired Learning for 3D Computational Periscopy](https://mlsanity.com/paper/2601.12257/) (2026)

## Abstract (arXiv)

The ability to form non-line-of-sight (NLOS) images of changing scenes could be transformative in a variety of fields, including search and rescue, autonomous vehicle navigation, and reconnaissance. Most existing active NLOS methods illuminate the hidden scene using a pulsed laser directed at a relay surface and collect time-resolved measurements of returning light. The prevailing approaches include raster scanning of a rectangular grid on a vertical wall opposite the volume of interest to generate a collection of confocal measurements. These are inherently limited by the need for laser scanning. Methods that avoid laser scanning track the moving parts of the hidden scene as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of objects in motion and a 'map' of the stationary scenery behind them. The ability to count, localize, and characterize the sizes of hidden objects in motion, combined with mapping of the stationary hidden scene, could greatly improve indoor situational awareness in a variety of applications.