  Inspired by the recent advances in implicitly representing signals with
trained neural networks, we aim to learn a continuous representation for
narrow-baseline 4D light fields. We propose an implicit representation model
for 4D light fields which is conditioned on a sparse set of input views. Our
model is trained to output the light field values for a continuous range of
query spatio-angular coordinates. Given a sparse set of input views, our scheme
can super-resolve the input in both spatial and angular domains by flexible
factors. consists of a feature extractor and a decoder which are trained on a
dataset of light field patches. The feature extractor captures per-pixel
features from the input views. These features can be resized to a desired
spatial resolution and fed to the decoder along with the query coordinates.
This formulation enables us to reconstruct light field views at any desired
spatial and angular resolution. Additionally, our network can handle scenarios
in which input views are either of low-resolution or with missing pixels.
Experiments show that our method achieves state-of-the-art performance for the
task of view synthesis while being computationally fast.
