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Title:

Keep it Unreal: Bridging the Realism Gap for 2.5 D Recognition with Geometry Priors Only

Document type:
Zeitschriftenaufsatz
Author(s):
Zakharov, S.; Planche, B.; Wu, Z.; Hutter, A.; Kosch, H.; Ilic, S.
Abstract:
With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images. However, discrepancies with the real data acquired from various depth sensors still noticeably impede progress. Previous works adopted unsupervised approaches to generate more realistic depth data, but they all require real scans for training, even if unlabeled. This still represents a strong requirement, especially when c...     »
Keywords:
CAMP,CAMPComputerVision,ComputerVision,3DV,DomainAdaptation,3DPoseEstimation
Journal title:
arXiv preprint arXiv:1804.09113
Year:
2018
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