  Most Neural Radiance Fields (NeRFs) exhibit limited generalization
capabilities, which restrict their applicability in representing multiple
scenes using a single model. To address this problem, existing generalizable
NeRF methods simply condition the model on image features. These methods still
struggle to learn precise global representations over diverse scenes since they
lack an effective mechanism for interacting among different points and views.
In this work, we unveil that 3D implicit representation learning can be
significantly improved by mask-based modeling. Specifically, we propose masked
ray and view modeling for generalizable NeRF (MRVM-NeRF), which is a
self-supervised pretraining target to predict complete scene representations
from partially masked features along each ray. With this pretraining target,
MRVM-NeRF enables better use of correlations across different points and views
as the geometry priors, which thereby strengthens the capability of capturing
intricate details within the scenes and boosts the generalization capability
across different scenes. Extensive experiments demonstrate the effectiveness of
our proposed MRVM-NeRF on both synthetic and real-world datasets, qualitatively
and quantitatively. Besides, we also conduct experiments to show the
compatibility of our proposed method with various backbones and its superiority
under few-shot cases.
