Viewport-Oriented Panoramic Image Inpainting

Zhuoyi Shang, Yanwei Liu, Guoyi Li, Yunjian Zhang, Jingbo Miao, Jinxia Liu, Liming Wang
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Abstract

Panoramic images are usually viewed through Head Mounted Displays (HMDs), which renders only a narrow field of view from the raw panoramic image. This distinctive viewing feature has largely been ignored when inpainting panoramic images. To address this issue, we propose a viewport-oriented generative adversarial panoramic image inpainting network in this paper. For capturing the distorted features accurately in the generating process of equirectangular projection (ERP) panoramic image, a latitude-adaptive feature fusion module is devised to aggregate the latitude-level features in ERP image and less-distorted patch-level viewport-domain features. Furthermore, a novel cross-domain discriminator is proposed to force the inpainting network to generate more plausible results in viewports. Extensive experiments show that our model achieves better performance compared to the baseline methods, especially in the viewport images.
面向viewport的全景图像绘制
全景图像通常是通过头戴式显示器(hmd)观看的,它只能从原始全景图像中呈现一个狭窄的视野。在绘制全景图像时,这种独特的观察特征在很大程度上被忽略了。为了解决这一问题,本文提出了一种面向视口的生成对抗全景图像绘制网络。为了准确捕获等矩形投影(ERP)全景图像生成过程中的畸变特征,设计了纬度自适应特征融合模块,将ERP图像中的纬度级特征与畸变较小的斑块级视口域特征进行融合。在此基础上,提出了一种新的跨域鉴别器,使喷漆网络在视口产生更可信的结果。大量的实验表明,与基线方法相比,我们的模型取得了更好的性能,特别是在视口图像中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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