  Inpainting has been continuously studied in the field of computer vision. As
artificial intelligence technology developed, deep learning technology was
introduced in inpainting research, helping to improve performance. Currently,
the input target of an inpainting algorithm using deep learning has been
studied from a single image to a video. However, deep learning-based inpainting
technology for panoramic images has not been actively studied. We propose a
360-degree panoramic image inpainting method using generative adversarial
networks (GANs). The proposed network inputs a 360-degree equirectangular
format panoramic image converts it into a cube map format, which has relatively
little distortion and uses it as a training network. Since the cube map format
is used, the correlation of the six sides of the cube map should be considered.
Therefore, all faces of the cube map are used as input for the whole
discriminative network, and each face of the cube map is used as input for the
slice discriminative network to determine the authenticity of the generated
image. The proposed network performed qualitatively better than existing
single-image inpainting algorithms and baseline algorithms.
