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MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects
[article]
2020
arXiv
pre-print
Preserved Fulltext
We propose Multi-Modal Gaussian Shape Descriptor (MMGSD), a new visual representation of deformable objects which extends ideas from dense object descriptors to predict all symmetric correspondences between ...
We explore learning pixelwise correspondences between images of deformable objects in different configurations. ...
Fig. 1 : 1 Multi-Modal Gaussian Shape Descriptors (MMGSD)
Fig. 2 : 2 Fig. 2: We visualize the multi-modal ground truth distributions (B) and predicted (A,C) correspondences on domain-randomized images ...
arXiv:2010.04339v1
fatcat:i6cq3b6p5zcgjonjupln3xl6re
VisuoSpatial Foresight for Physical Sequential Fabric Manipulation
[article]
2021
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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In this earlier work, we evaluated VSF on multi-step fabric smoothing and folding tasks against 5 baseline methods in simulation and on the da Vinci Research Kit (dVRK) surgical robot without any demonstrations ...
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. ...
Ganapathi A, Sundaresan P, Thananjeyan B, Balakrishna A, Seita D, Hoque R, Gonzalez JE, Goldberg K (2020) Mmgsd: Multi-modal gaussian shape descriptors for correspondence matching in 1d and 2d deformable ...
arXiv:2102.09754v2
fatcat:kyxkizg7ofg2vbsor6q6jclf3y