  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.
