A Generative Model for Generic Light Field Reconstruction

Understand

Recently deep generative models have achieved impressive progress in modeling the distribution of training data.

• In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data distribution of light field patches.
• We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks.
• While pure learning-based approaches do achieve excellent results on each instance of such a problem, their applicability is limited to the specific observation model they have been trained on.