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Robust Sim2Real 3D Object Classification Using Graph Representations and a Deep Center Voting Scheme | IEEE Journals & Magazine | IEEE Xplore

Robust Sim2Real 3D Object Classification Using Graph Representations and a Deep Center Voting Scheme


Abstract:

While object semantic understanding is essential for service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviat...Show More

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.
Published in: IEEE Robotics and Automation Letters ( Volume: 7, Issue: 3, July 2022)
Page(s): 8028 - 8035
Date of Publication: 27 June 2022

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