我们能否通过全景分割来满足视障者的导航感知需求？
Can we cover navigational perception needs of the visually impaired by panoptic segmentation?
摘要：
视觉障碍者的导航感知已经通过经典的和基于深度学习的分割方法得到了实质性的提升。在经典的视觉识别方法中，分割模型大多是对象相关的，这意味着必须为感兴趣的对象设计特定的算法。相比之下，基于深度学习的模型（诸如实例分割和语义分割）允许为盲人个体单独地识别整个场景的一部分，即事物或东西。然而，两者都不能为视障人士提供对周围环境的整体理解。全景分割是一种新提出的视觉模型，其目标是统一语义分割和实例分割。出于这一动机，我们建议利用全景分割作为一种方法来导航视力受损的人，通过提供在视力受损的人附近的东西和东西的意识。我们证明，全景分割是能够配备一个整体的现实世界的场景感知，通过可穿戴的辅助系统的视觉障碍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
