  We present a novel technique for implicit neural representation of light
fields at continuously defined viewpoints with high quality and fidelity. Our
implicit neural representation maps 4D coordinates defining two-plane
parameterization of the light fields to the corresponding color values. We
leverage periodic activations to achieve high expressivity and accurate
reconstruction for complex data manifolds while keeping low storage and
inference time requirements. However, na\"ively trained non-3D structured
networks do not adequately satisfy the multi-view consistency; instead, they
perform alpha blending of nearby viewpoints. In contrast, our View
Correspondence Network, or VICON, leverages stereo matching, optimization by
automatic differentiation with respect to the input space, and multi-view pixel
correspondence to provide a novel implicit representation of the light fields
faithful to the novel views that are unseen during the training. Experimental
results show VICON superior to the state-of-the-art non-3D implicit light field
representations both qualitatively and quantitatively. Moreover, our implicit
representation captures a larger field of view (FoV), surpassing the extent of
the observable scene by the cameras of the ground truth renderings.
