  Navigational perception for visually impaired people has been substantially
promoted by both classic and deep learning based segmentation methods. In
classic visual recognition methods, the segmentation models are mostly
object-dependent, which means a specific algorithm has to be devised for the
object of interest. In contrast, deep learning based models such as instance
segmentation and semantic segmentation allow to individually recognize part of
the entire scene, namely things or stuff, for blind individuals. However, both
of them can not provide a holistic understanding of the surroundings for the
visually impaired. Panoptic segmentation is a newly proposed visual model with
the aim of unifying semantic segmentation and instance segmentation. Motivated
by that, we propose to utilize panoptic segmentation as an approach to
navigating visually impaired people by offering both things and stuff awareness
in the proximity of the visually impaired. We demonstrate that panoptic
segmentation is able to equip the visually impaired with a holistic real-world
scene perception through a wearable assistive system.
