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Learning to Estimat...
Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images
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- Kokic, Mia, 1992- (författare)
- KTH,Centrum för autonoma system, CAS,Robotik, perception och lärande, RPL
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- Kragic, Danica, 1971- (författare)
- KTH,Robotik, perception och lärande, RPL,Centrum för autonoma system, CAS
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- Bohg, Jeannette (författare)
- Stanford University, Computer Science Department, CA, USA
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2019
- 2019
- Engelska.
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3980-3987
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape estimation called HOPS-Net and utilize prior work to estimate the hand pose and configuration. We leverage the insight that information about the hand facilitates object pose and shape estimation by incorporating the hand into both training and inference of the object pose and shape as well as the refinement of the estimated pose. The network is trained on a large synthetic dataset of objects in interaction with a human hand. To bridge the gap between real and synthetic images, we employ an image-to-image translation model (Augmented CycleGAN) that generates realistically textured objects given a synthetic rendering. This provides a scalable way of generating annotated data for training HOPS-Net. Our quantitative experiments show that even noisy hand parameters significantly help object pose and shape estimation. The qualitative experiments show results of pose and shape estimation of objects held by a hand 'in the wild'.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Convolutional neural networks
- Large dataset
- Textures
- Image translation
- Object interactions
- Qualitative experiments
- Quantitative experiments
- Shape estimation
- Synthetic images
- Synthetic rendering
- Textured objects
- Intelligent robots
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)