  We propose a method for reconstructing a continuous light field of a target
scene from a single observed image. Our method takes the best of two worlds:
joint aperture-exposure coding for compressive light-field acquisition, and a
neural radiance field (NeRF) for view synthesis. Joint aperture-exposure coding
implemented in a camera enables effective embedding of 3-D scene information
into an observed image, but in previous works, it was used only for
reconstructing discretized light-field views. NeRF-based neural rendering
enables high quality view synthesis of a 3-D scene from continuous viewpoints,
but when only a single image is given as the input, it struggles to achieve
satisfactory quality. Our method integrates these two techniques into an
efficient and end-to-end trainable pipeline. Trained on a wide variety of
scenes, our method can reconstruct continuous light fields accurately and
efficiently without any test time optimization. To our knowledge, this is the
first work to bridge two worlds: camera design for efficiently acquiring 3-D
information and neural rendering.
