  We present a method that tackles the challenge of predicting color and depth
behind the visible content of an image. Our approach aims at building up a
Layered Depth Image (LDI) from a single RGB input, which is an efficient
representation that arranges the scene in layers, including originally occluded
regions. Unlike previous work, we enable an adaptive scheme for the number of
layers and incorporate semantic encoding for better hallucination of partly
occluded objects. Additionally, our approach is object-driven, which especially
boosts the accuracy for the occluded intermediate objects. The framework
consists of two steps. First, we individually complete each object in terms of
color and depth, while estimating the scene layout. Second, we rebuild the
scene based on the regressed layers and enforce the recomposed image to
resemble the structure of the original input. The learned representation
enables various applications, such as 3D photography and diminished reality,
all from a single RGB image.
