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

Jonathan Klein, Martin Laurenzis, Dominik L. Michels, and Matthias B. Hullin


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.


In: British Machine Vision Conference (BMVC), 2018



Supplementary Material

The supplemental material contains more detailed descriptions of the challenges, data formats, metrics, and tools.