<div class="csl-bib-body">
<div class="csl-entry">Weibel, J. B., Patten, T., & Vincze, M. (2022). Robust Sim2Real 3D Object Classification Using Graph Representations and a Deep Center Voting Scheme. <i>IEEE Robotics and Automation Letters</i>, <i>7</i>(3), 8028–8035. https://doi.org/10.1109/LRA.2022.3186745</div>
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dc.identifier.issn
2377-3766
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/135961
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dc.description.abstract
While object semantic understanding is essential for service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of the annotation necessary to approach this problem, but today's methods still struggle with the differences between artificial and real 3D data. We conjecture that one of the causes of this issue is the fact that today's methods learn directly from point coordinates, which makes them highly sensitive to scale changes. We propose to learn from a graph of reproducible object parts whose scale is more reliable. In combination with a voting scheme, our approach achieves significantly more robust classification and improves upon state-of-the-art by up to 16% when transferring from artificial to real objects.
en
dc.description.sponsorship
European Commission
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dc.description.sponsorship
Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
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dc.language.iso
en
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dc.publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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dc.relation.ispartof
IEEE Robotics and Automation Letters
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dc.subject
deep learning for visual perception
en
dc.subject
recognition
en
dc.subject
visual learning
en
dc.title
Robust Sim2Real 3D Object Classification Using Graph Representations and a Deep Center Voting Scheme