Joint Neural Representation for Multiple Light Fields

Neural implicit representations have appeared as a promising technique for representing a variety of signals, among which light fields. These representations offer several advantages over traditional grid-based representations, such as independence to the signal resolution. Some work has been done to find good initial representations for a given type of signal, usually via meta-learning approaches. However, exploiting the features shared between different scenes remains an understudied problem. We provide a step towards this end by presenting a method for sharing the representation between thousands of light fields, splitting the representation between a part that is shared between all light fields and a part which varies individually from one light field to another. We show that this joint representation possesses good interpolation properties, and allows for a more light-weight storage of a whole database of light fields, exhibiting a ten-fold reduction in the size of the representation when compared to using a separate representation for each light field.