A Quantitative Platform for Non-Line-of-Sight Imaging Problems
Jonathan Klein, Martin Laurenzis, Dominik L. Michels, and Matthias B. Hullin
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.
Paper
In: British Machine Vision Conference (BMVC), 2018
Link: https://nlos.cs.uni-bonn.de/paper
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Supplementary Material
The supplemental material contains more detailed descriptions of the challenges, data formats, metrics, and tools.
