Currently, single image inpainting has achieved promising results based on
deep convolutional neural networks. However, inpainting on stereo images with
missing regions has not been explored thoroughly, which is also a significant
but different problem. One crucial requirement for stereo image inpainting is
stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross
Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a
Geometry-Aware Attention (GAA) module and an Iterative Cross Guidance (ICG)
strategy. The GAA module relies on the epipolar geometry cues and learns the
geometry-aware guidance from one view to another, which is beneficial to make
the corresponding regions in two views consistent. However, learning guidance
from co-existing missing regions is challenging. To address this issue, the ICG
strategy is proposed, which can alternately narrow down the missing regions of
the two views in an iterative manner. Experimental results demonstrate that our
proposed network outperforms the latest stereo image inpainting model and
state-of-the-art single image inpainting models.

该论文提出了一种使用迭代式几何感知交叉引导网络实现立体图像修复的方法，该方法包含几何感知注意力模块和迭代交叉引导策略，可以提高两个视图之间对应区域的一致性，实现对立体图像的修复。实验结果表明，与最新的立体图像修复模型和最先进的单张图片修复模型相比，我们提出的网络表现更好。