# Onsite Non-Line-of-Sight Imaging via Online Calibrations

arXiv: https://arxiv.org/abs/2112.14555 · HTML: https://mlsanity.com/paper/2112.14555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [Non-line-of-Sight Imaging via Neural Transient Fields](https://mlsanity.com/paper/2101.00373/) (2021)
- [Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar](https://mlsanity.com/paper/1912.06613/) (2019)
- [Photon-Flooded Single-Photon 3D Cameras](https://mlsanity.com/paper/1903.08347/) (2019)
- [A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging](https://mlsanity.com/paper/1703.02635/) (2017)
- [Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective](https://mlsanity.com/paper/1611.00939/) (2016)
- [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)
- [Efficient Non-Line-of-Sight Imaging from Transient Sinograms](https://mlsanity.com/paper/2008.02787/) (2020)
- [Non-line-of-sight tracking of people at long range](https://mlsanity.com/paper/1703.02124/) (2017)
- [Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes](https://mlsanity.com/paper/2104.13807/) (2021)
- [A Calibration Scheme for Non-Line-of-Sight Imaging Setups](https://mlsanity.com/paper/1912.09923/) (2019)

## Abstract (arXiv)

There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle. Existing solutions generally pre-calibrate the system before scanning the hidden objects. Onsite adjustments of the occluder, object and scanning pattern require re-calibration. We present an online calibration technique that directly decouples the acquired transients at onsite scanning into the LOS and hidden components. We use the former to directly (re)calibrate the system upon changes of scene/obstacle configurations, scanning regions, and scanning patterns whereas the latter for hidden object recovery via spatial, frequency or learning based techniques. Our technique avoids using auxiliary calibration apparatus such as mirrors or checkerboards and supports both laboratory validations and real-world deployments.