A Quantitative Platform for Non-Line-of-Sight Imaging Problems
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.
Abstract
The computational sensing community has recently seen a surge of works on imaging beyond the direct line of sight. However, most of the reported results rely on drastically different measurement setups and algorithms, and are therefore hard to impossible to compare quantitatively. In this paper, we focus on an important class of approaches, namely those that aim to reconstruct scene properties from time-resolved optical impulse responses. We introduce a collection of reference data and quality metrics that are tailored to the most common use cases, and we define reconstruction challenges that we hope will aid the development and assessment of future methods.Files
- Full Paper (PDF): KleinEtAl-NLOSChallenge-BMVC2018.pdf
- Supplemental Document (PDF): KleinEtAl-NLOSChallenge-BMVC2018-supp.pdf
BibTeX Citation
@inproceedings{KleinEtAl-NLOSChallenge-BMVC2018,
author = {Jonathan Klein and
Martin Laurenzis and
Dominik L. Michels and
Matthias B. Hullin},
title = {A Quantitative Platform for Non-Line-of-Sight Imaging Problems},
booktitle = {British Machine Vision Conference 2018, {BMVC} 2018, Northumbria University,
Newcastle, UK, September 3-6, 2018},
pages = {104},
year = {2018},
}