The automated analysis of visual data is a key enabler for industrial and consumer technologies and of immense economic and social importance. Its main challenge is in the inherent ambiguity of images due to the very mechanism of image capture: light reaching a pixel on different paths or at different times is mixed irreversibly. Consequently, even after decades of extensive research, problems like deblurring or descattering, geometry/material estimation or motion tracking are still largely unsolved and will remain so in the foreseeable
Transient imaging (TI) tackles this problem by recording ultrafast optical echoes that unmix light contributions by the total pathlength. So far, TI used to require high-end measurement setups. By introducing computational TI (CTI), we paved the way for a lightweight capture of transient data using consumer hardware. We showed the potential of CTI in scenarios like robust range measurement, descattering and imaging of objects outside the line of sight – tasks that had been considered difficult to impossible so far.
The ECHO project is rooted in computer graphics and computational imaging. In it, we will overcome the practical limitations that are hampering a large-scale deployment of TI: the time required for data capture and to reconstruct the desired information, both in the order of seconds to minutes, a lack of dedicated image priors and of quality guarantees for the reconstruction, the limited accuracy and performance of forward models and the lack of ground-truth data and benchmark methods.
Over the course of ECHO, we will pioneer advanced capture setups and strategies, formation models, priors and numerical methods, for the first time enabling real-time reconstruction and analysis of transient light transport in complex and dynamic scenes. The methodology developed in this far-reaching project will turn TI from a research technology into a family of practical tools that will immediately benefit many applications.
ECHO is funded by ERC Starting Grant 802192 (funding period 12/2018 – 11/2023).
Clara Callenberg, Ashley Lyons, Dennis den Brok, Areeba Fatima, Alejandro Turpin, Vytautas Zickus, Laura M. Machesky, Jamie A. Whitelaw, Daniele Faccio, Matthias B. Hullin Scientific Reports (Nature Publishing Group) 11, 1689 (2021), https://doi.org/10.1038/s41598-021-81159-x, 2021.
Jonathan Klein, Martin Laurenzis, Dominik L. Michels, Matthias B. Hullin In Proceedings of British Machine Vision Conference (BMVC 2018), Northumbria University, Newcastle, UK, September 3-6, 2018, 2018.
In this paper, we present a reference database of time-resolved light echoes for non-line-of-sight sensing.