Omnidirectional image (ODI) data is captured with a 360x180 field-of-view, which is much wider than the pinhole cameras and contains richer spatial information than the conventional planar images. Accordingly, omnidirectional vision has attracted booming attention due to its more advantageous performance in numerous applications, such as autonomous driving and virtual reality. In recent years, the availability of customer-level 360 cameras has made omnidirectional vision more popular, and the advance of deep learning (DL) has significantly sparked its research and applications. This paper presents a systematic and comprehensive review and analysis of the recent progress in DL methods for omnidirectional vision. Our work covers four main contents: (i) An introduction to the principle of omnidirectional imaging, the convolution methods on the ODI, and datasets to highlight the differences and difficulties compared with the 2D planar image data; (ii) A structural and hierarchical taxonomy of the DL methods for omnidirectional vision; (iii) A summarization of the latest novel learning strategies and applications; (iv) An insightful discussion of the challenges and open problems by highlighting the potential research directions to trigger more research in the community.
翻译:光向图像(ODI)数据以360x180的视野实地图像(ODI)数据采集,这比针孔相机的范围要广得多,并包含比常规平板图像更丰富的空间信息。因此,全向图像在许多应用中由于在自主驱动和虚拟现实等更有利的性能而引起人们的高度关注。近年来,客户级360摄像头的可用性使全向图像更加受欢迎,深层次学习的进步(DL)极大地激发了其研究和应用。本文件对光向图像DL方法的最新进展进行了系统、全面的审查和分析。我们的工作涵盖四个主要内容:(一) 介绍全向成像原则、ODI的演进方法,以及数据组,以突出与2D平面图像数据相比的差异和困难;(二) 对全向视觉DL方法的结构和等级分类;三) 对全向视觉的最新新学习战略和应用进行总结和分析。我们的工作涉及四个主要内容:(四) 介绍全向成型研究方向,通过更开放的研究讨论,突出社区的潜在研究方向。