Recently, convolutional neural networks (CNN) have been successfully applied
to view synthesis problems. However, such CNN-based methods can suffer from
lack of texture details, shape distortions, or high computational complexity.
In this paper, we propose a novel CNN architecture for view synthesis called
"Deep View Morphing" that does not suffer from these issues. To synthesize a
middle view of two input images, a rectification network first rectifies the
two input images. An encoder-decoder network then generates dense
correspondences between the rectified images and blending masks to predict the
visibility of pixels of the rectified images in the middle view. A view
morphing network finally synthesizes the middle view using the dense
correspondences and blending masks. We experimentally show the proposed method
significantly outperforms the state-of-the-art CNN-based view synthesis method.

本文提出了一种全新的卷积神经网络结构 ——Deep View Morphing，用于视图合成问题。该方法通过矫正网络和编解码网络等步骤，生成密集的对应关系和混合遮罩，有效解决了传统 CNN 方法中存在的纹理细节、形状失真和高计算复杂度等问题，实验结果表明该方法优于当前基于 CNN 的视图合成方法。