  Light fields are 4D scene representation typically structured as arrays of
views, or several directional samples per pixel in a single view. This highly
correlated structure is not very efficient to transmit and manipulate
(especially for editing), though. To tackle these problems, we present a novel
compact and editable light field representation, consisting of a set of visual
channels (i.e. the central RGB view) and a complementary meta channel that
encodes the residual geometric and appearance information. The visual channels
in this representation can be edited using existing 2D image editing tools,
before accurately reconstructing the whole edited light field back. We propose
to learn this representation via an autoencoder framework, consisting of an
encoder for learning the representation, and a decoder for reconstructing the
light field. To handle the challenging occlusions and propagation of edits, we
specifically designed an editing-aware decoding network and its associated
training strategy, so that the edits to the visual channels can be consistently
propagated to the whole light field upon reconstruction.Experimental results
show that our proposed method outperforms related existing methods in
reconstruction accuracy, and achieves visually pleasant performance in editing
propagation.
