  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.
