Embodied Uncertainty-Aware Object Segmentation

Understand

We introduce uncertainty-aware object instance segmentation (UncOS) and demonstrate its usefulness for embodied interactive segmentation.

• To deal with uncertainty in robot perception, we propose a method for generating a hypothesis distribution of object segmentation.
• We obtain a set of region-factored segmentation hypotheses together with confidence estimates by making multiple queries of large pre-trained models.
• This process can produce segmentation results that achieve state-of-the-art performance on unseen object segmentation problems.