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

dc.conference.date2018-09-06
dc.conference.locationNewcastle, UK
dc.conference.nameBRITISH MACHINE VISION CONFERENCE
dc.contributor.authorKlein, Jonathan
dc.contributor.authorLaurenzis, Martin
dc.contributor.authorMichels, Dominik L.
dc.contributor.authorHullin, Matthias B.
dc.contributor.departmentVCC Analytics Research Group
dc.contributor.institutionUniversity of Bonn
dc.contributor.institutionFrench-German Research Institute of Saint-Louis (ISL)
dc.date.accessioned2018-11-27T10:49:56Z
dc.date.available2018-11-27T10:49:56Z
dc.date.issued2018-09-06
dc.description.abstractThe 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.
dc.eprint.versionPost-print
dc.identifier.urihttp://hdl.handle.net/10754/630081
dc.relation.urlhttp://bmvc2018.org/contents/papers/0363.pdf
dc.titleA Quantitative Platform for Non-Line-of-Sight Imaging Problems
dc.typeConference Paper
display.details.left<span><h5>Type</h5>Conference Paper<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Klein, Jonathan,equals">Klein, Jonathan</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Laurenzis, Martin,equals">Laurenzis, Martin</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0002-1621-325X&spc.sf=dc.date.issued&spc.sd=DESC">Michels, Dominik L.</a> <a href="https://orcid.org/0000-0002-1621-325X" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Hullin, Matthias B.,equals">Hullin, Matthias B.</a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=VCC Analytics Research Group,equals">VCC Analytics Research Group</a><br><br><h5>Date</h5>2018-09-06</span>
display.details.right<span><h5>Abstract</h5>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.<br><br><h5>Conference/Event Name</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.conference=BRITISH MACHINE VISION CONFERENCE,equals">BRITISH MACHINE VISION CONFERENCE</a><br><br><h5>Additional Links</h5>http://bmvc2018.org/contents/papers/0363.pdf</span>
kaust.personMichels, Dominik L.
orcid.authorKlein, Jonathan
orcid.authorLaurenzis, Martin
orcid.authorMichels, Dominik L.::0000-0002-1621-325X
orcid.authorHullin, Matthias B.
orcid.id0000-0002-1621-325X
refterms.dateFOA2018-11-27T10:49:57Z
Files
Original bundle
Now showing 1 - 2 of 2
Loading...
Thumbnail Image
Name:
0363.pdf
Size:
3.02 MB
Format:
Adobe Portable Document Format
Description:
Paper
Loading...
Thumbnail Image
Name:
0363_supp.pdf
Size:
4.03 MB
Format:
Adobe Portable Document Format
Description:
Supplementary information