  We present Omnidirectional Neural Radiance Fields (OmniNeRF), the first
method to the application of parallax-enabled novel panoramic view synthesis.
Recent works for novel view synthesis focus on perspective images with limited
field-of-view and require sufficient pictures captured in a specific condition.
Conversely, OmniNeRF can generate panorama images for unknown viewpoints given
a single equirectangular image as training data. To this end, we propose to
augment the single RGB-D panorama by projecting back and forth between a 3D
world and different 2D panoramic coordinates at different virtual camera
positions. By doing so, we are able to optimize an Omnidirectional Neural
Radiance Field with visible pixels collecting from omnidirectional viewing
angles at a fixed center for the estimation of new viewing angles from varying
camera positions. As a result, the proposed OmniNeRF achieves convincing
renderings of novel panoramic views that exhibit the parallax effect. We
showcase the effectiveness of each of our proposals on both synthetic and
real-world datasets.
