Fully Self-Supervised Class Awareness in Dense Object Descriptors

10/05/2021
by   Denis Hadjivelichkov, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset