Existing view synthesis methods mainly focus on the perspective images and
have shown promising results. However, due to the limited field-of-view of the
pinhole camera, the performance quickly degrades when large camera movements
are adopted. In this paper, we make the first attempt to generate novel views
from a single indoor panorama and take the large camera translations into
consideration. To tackle this challenging problem, we first use Convolutional
Neural Networks (CNNs) to extract the deep features and estimate the depth map
from the source-view image. Then, we leverage the room layout prior, a strong
structural constraint of the indoor scene, to guide the generation of target
views. More concretely, we estimate the room layout in the source view and
transform it into the target viewpoint as guidance. Meanwhile, we also
constrain the room layout of the generated target-view images to enforce
geometric consistency. To validate the effectiveness of our method, we further
build a large-scale photo-realistic dataset containing both small and large
camera translations. The experimental results on our challenging dataset
demonstrate that our method achieves state-of-the-art performance. The project
page is at this https URL

本文提出一种从单个室内全景图生成新视角的方法，在使用卷积神经网络提取深层特征和估计深度图的基础上，利用室内场景的布局信息指导目标视角图像的生成，并加入几何约束以提高一致性和鲁棒性。实验结果表明，该方法在小大相机移动下均有较好效果。