Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish valid pixels from invalid pixels and find suitable references for corrupted areas, thus leading to artifacts in the inpainted results. In response to these challenges, we propose a panoramic image inpainting framework that consists of a Face Generator, a Cube Generator, a side branch, and two discriminators. We use the Cubemap Projection (CMP) format as network input. The generator employs gated convolutions to distinguish valid pixels from invalid ones, while a side branch is designed utilizing contextual reconstruction (CR) loss to guide the generators to find the most suitable reference patch for inpainting the missing region. The proposed method is compared with state-of-the-art (SOTA) methods on SUN360 Street View dataset in terms of PSNR and SSIM. Experimental results and ablation study demonstrate that the proposed method outperforms SOTA both quantitatively and qualitatively.

翻译：基于深度学习的方法在全景图像修复任务中已展现出令人振奋的成果。然而，现有方法在区分有效像素与无效像素、为受损区域寻找合适参考方面仍面临挑战，导致修复结果中存在伪影。针对这些问题，我们提出了一种全景图像修复框架，包含人脸生成器、立方体生成器、侧分支以及两个判别器。采用立方体贴图投影（CMP）格式作为网络输入。生成器通过门控卷积区分有效像素与无效像素，同时设计了利用上下文重建（CR）损失的侧分支，引导生成器为缺失区域寻找最合适的参考补丁。将所提方法与当前最优（SOTA）方法在SUN360街景数据集上进行了PSNR和SSIM指标的对比。实验结果与消融研究表明，所提方法在定量与定性指标上均优于SOTA方法。