  We propose a method for converting a single RGB-D input image into a 3D photo
- a multi-layer representation for novel view synthesis that contains
hallucinated color and depth structures in regions occluded in the original
view. We use a Layered Depth Image with explicit pixel connectivity as
underlying representation, and present a learning-based inpainting model that
synthesizes new local color-and-depth content into the occluded region in a
spatial context-aware manner. The resulting 3D photos can be efficiently
rendered with motion parallax using standard graphics engines. We validate the
effectiveness of our method on a wide range of challenging everyday scenes and
show fewer artifacts compared with the state of the arts.
