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

Light Field Implicit Representation for Flexible Resolution Reconstruction