  Neural Radiance Fields (NeRFs) have shown impressive results for novel view
synthesis when a sufficiently large amount of views are available. When dealing
with few-shot settings, i.e. with a small set of input views, the training
could overfit those views, leading to artifacts and geometric and chromatic
inconsistencies in the resulting rendering. Regularization is a valid solution
that helps NeRF generalization. On the other hand, each of the most recent NeRF
regularization techniques aim to mitigate a specific rendering problem.
Starting from this observation, in this paper we propose CombiNeRF, a framework
that synergically combines several regularization techniques, some of them
novel, in order to unify the benefits of each. In particular, we regularize
single and neighboring rays distributions and we add a smoothness term to
regularize near geometries. After these geometric approaches, we propose to
exploit Lipschitz regularization to both NeRF density and color networks and to
use encoding masks for input features regularization. We show that CombiNeRF
outperforms the state-of-the-art methods with few-shot settings in several
publicly available datasets. We also present an ablation study on the LLFF and
NeRF-Synthetic datasets that support the choices made. We release with this
paper the open-source implementation of our framework.
