  Synthesizing a densely sampled light field from a single image is highly
beneficial for many applications. The conventional method reconstructs a depth
map and relies on physical-based rendering and a secondary network to improve
the synthesized novel views. Simple pixel-based loss also limits the network by
making it rely on pixel intensity cue rather than geometric reasoning. In this
study, we show that a different geometric representation, namely, appearance
flow, can be used to synthesize a light field from a single image robustly and
directly. A single end-to-end deep neural network that does not require a
physical-based approach nor a post-processing subnetwork is proposed. Two novel
loss functions based on known light field domain knowledge are presented to
enable the network to preserve the spatio-angular consistency between
sub-aperture images effectively. Experimental results show that the proposed
model successfully synthesizes dense light fields and qualitatively and
quantitatively outperforms the previous model . The method can be generalized
to arbitrary scenes, rather than focusing on a particular class of object. The
synthesized light field can be used for various applications, such as depth
estimation and refocusing.
