A Quantitative Platform for Non-Line-of-Sight Imaging Problems

In: Proceedings of the British Machine Vision Conference 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.

Images

Bibtex

@ARTICLE{Klein2018,
    author = {Klein, Jonathan and Laurenzis, Martin and Michels, Dominik L. and Hullin, Matthias B.},
     title = {A Quantitative Platform for Non-Line-of-Sight Imaging Problems},
   journal = {Proceedings of the British Machine Vision Conference 2018},
      year = {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.},
       url = {https://nlos.cs.uni-bonn.de/paper}
}