  3D reconstruction from images has wide applications in Virtual Reality and
Automatic Driving, where the precision requirement is very high.
Ground-breaking research in the neural radiance field (NeRF) by utilizing
Multi-Layer Perceptions has dramatically improved the representation quality of
3D objects. Some later studies improved NeRF by building truncated signed
distance fields (TSDFs) but still suffer from the problem of blurred surfaces
in 3D reconstruction. In this work, this surface ambiguity is addressed by
proposing a novel way of 3D shape representation, OmniNeRF. It is based on
training a hybrid implicit field of Omni-directional Distance Field (ODF) and
neural radiance field, replacing the apparent density in NeRF with
omnidirectional information. Moreover, we introduce additional supervision on
the depth map to further improve reconstruction quality. The proposed method
has been proven to effectively deal with NeRF defects at the edges of the
surface reconstruction, providing higher quality 3D scene reconstruction
results.
