The view synthesis problem--generating novel views of a scene from known
imagery--has garnered recent attention due in part to compelling applications
in virtual and augmented reality. In this paper, we explore an intriguing
scenario for view synthesis: extrapolating views from imagery captured by
narrow-baseline stereo cameras, including VR cameras and now-widespread
dual-lens camera phones. We call this problem stereo magnification, and propose
a learning framework that leverages a new layered representation that we call
multiplane images (MPIs). Our method also uses a massive new data source for
learning view extrapolation: online videos on YouTube. Using data mined from
such videos, we train a deep network that predicts an MPI from an input stereo
image pair. This inferred MPI can then be used to synthesize a range of novel
views of the scene, including views that extrapolate significantly beyond the
input baseline. We show that our method compares favorably with several recent
view synthesis methods, and demonstrate applications in magnifying
narrow-baseline stereo images.

本文提出了一种利用在线视频数据来学习视角外推的新方法，通过将输入的立体图像对转化为 “多层图像”，再利用深度学习网络来生成视角外推的图像，实现了将狭窄基线的立体图像进行放大的功能。