论文标题
PIINET：使用立方体地图的360度全景图像介绍网络
PIINET: A 360-degree Panoramic Image Inpainting Network Using a Cube Map
论文作者
论文摘要
在计算机视觉领域，对介质进行了不断的研究。随着人工智能技术的发展，深度学习技术是在介绍研究中引入的，有助于提高性能。当前，已经研究了从单个图像到视频，研究了使用深度学习的介入算法的输入目标。但是，尚未对全景图像进行深度学习的研究技术。我们建议使用生成对抗网络（GAN）提出一种360度全景图像。提出的网络输入一个360度等式格式的全景图像将其转换为立方体地图格式，该格式的失真相对较小，并将其用作训练网络。由于使用了立方体图格式，因此应考虑立方体图的六个侧面的相关性。因此，立方体图的所有面都用作整个判别网络的输入，并且Cube Map的每个面都用作切片判别网络的输入，以确定生成图像的真实性。所提出的网络在定性上的执行效果要比现有的单图像插入算法和基线算法更好。
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.
