  Photo-realistic neural reconstruction and rendering of the human portrait are
critical for numerous VR/AR applications. Still, existing solutions inherently
rely on multi-view capture settings, and the one-shot solution to get rid of
the tedious multi-view synchronization and calibration remains extremely
challenging. In this paper, we propose MirrorNeRF - a one-shot neural portrait
free-viewpoint rendering approach using a catadioptric imaging system with
multiple sphere mirrors and a single high-resolution digital camera, which is
the first to combine neural radiance field with catadioptric imaging so as to
enable one-shot photo-realistic human portrait reconstruction and rendering, in
a low-cost and casual capture setting. More specifically, we propose a
light-weight catadioptric system design with a sphere mirror array to enable
diverse ray sampling in the continuous 3D space as well as an effective online
calibration for the camera and the mirror array. Our catadioptric imaging
system can be easily deployed with a low budget and the casual capture ability
for convenient daily usages. We introduce a novel neural warping radiance field
representation to learn a continuous displacement field that implicitly
compensates for the misalignment due to our flexible system setting. We further
propose a density regularization scheme to leverage the inherent geometry
information from the catadioptric data in a self-supervision manner, which not
only improves the training efficiency but also provides more effective density
supervision for higher rendering quality. Extensive experiments demonstrate the
effectiveness and robustness of our scheme to achieve one-shot photo-realistic
and high-quality appearance free-viewpoint rendering for human portrait scenes.
