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.

基于深度学习的方法在全景图像修复任务中显示出令人鼓舞的结果，但现有方法难以区分有效像素与无效像素并找到适当的参考区域，从而导致修复结果中出现伪影。针对这些挑战，我们提出了一种全景图像修复框架，包括一个面部生成器、一个立方体生成器、一个附属分支和两个判别器。我们使用 Cubemap 投影格式作为网络输入。生成器通过门控卷积区分有效像素和无效像素，而附属分支采用上下文重建损失来指导生成器找到最适合修复缺失区域的参考补丁。在 PSNR 和 SSIM 方面，将所提出的方法与最先进的方法 (SOTA) 在 SUN360 Street View 数据集上进行比较。实验结果和消融分析表明，所提出的方法在定量和定性方面均优于 SOTA。