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Unseen Object Instance Segmentation for Robotic Environments
[article]
2021
arXiv
pre-print
In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of large-scale real-world dataset required for this task typically does not exist for most robotic settings, which motivates the use of synthetic data. Our proposed method, UOIS-Net, separately leverages synthetic RGB and synthetic depth for unseen object
arXiv:2007.08073v2
fatcat:urn5ojcgw5gfxjeuuoqwcq3ezm