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Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
[article]
2022
arXiv
pre-print
Tracking the 6D pose of objects in video sequences is important for robot manipulation. This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pose tracking. It aims to identify the optimal relative pose given the current RGB-D observation and a synthetic image conditioned on the previous best estimate and the object's model. The key contribution in this context is a novel neural network architecture, which appropriately disentangles the feature encoding to
arXiv:2105.14391v2
fatcat:6pxpnun7ffe77iyn47zzhchbhy