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- W3203516563 abstract "We address the problem of inferring self-supervised dense semantic correspondences between objects in multi-object scenes. The method introduces learning of class-aware dense object descriptors by providing either unsupervised discrete labels or confidence in object similarities. We quantitatively and qualitatively show that the introduced method outperforms previous techniques with more robust pixel-to-pixel matches. An example robotic application is also shown~- grasping of objects in clutter based on corresponding points." @default.
- W3203516563 created "2021-10-11" @default.
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- W3203516563 date "2021-10-05" @default.
- W3203516563 modified "2023-09-27" @default.
- W3203516563 title "Fully Self-Supervised Class Awareness in Dense Object Descriptors." @default.
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