  Novel view synthesis aims to synthesize new images from different viewpoints
of given images. Most of previous works focus on generating novel views of
certain objects with a fixed background. However, for some applications, such
as virtual reality or robotic manipulations, large changes in background may
occur due to the egomotion of the camera. Generated images of a large-scale
environment from novel views may be distorted if the structure of the
environment is not considered. In this work, we propose a novel fully
convolutional network, that can take advantage of the structural information
explicitly by incorporating the inverse depth features. The inverse depth
features are obtained from CNNs trained with sparse labeled depth values. This
framework can easily fuse multiple images from different viewpoints. To fill
the missing textures in the generated image, adversarial loss is applied, which
can also improve the overall image quality. Our method is evaluated on the
KITTI dataset. The results show that our method can generate novel views of
large-scale scene without distortion. The effectiveness of our approach is
demonstrated through qualitative and quantitative evaluation.
