  We present MVSNeRF, a novel neural rendering approach that can efficiently
reconstruct neural radiance fields for view synthesis. Unlike prior works on
neural radiance fields that consider per-scene optimization on densely captured
images, we propose a generic deep neural network that can reconstruct radiance
fields from only three nearby input views via fast network inference. Our
approach leverages plane-swept cost volumes (widely used in multi-view stereo)
for geometry-aware scene reasoning, and combines this with physically based
volume rendering for neural radiance field reconstruction. We train our network
on real objects in the DTU dataset, and test it on three different datasets to
evaluate its effectiveness and generalizability. Our approach can generalize
across scenes (even indoor scenes, completely different from our training
scenes of objects) and generate realistic view synthesis results using only
three input images, significantly outperforming concurrent works on
generalizable radiance field reconstruction. Moreover, if dense images are
captured, our estimated radiance field representation can be easily fine-tuned;
this leads to fast per-scene reconstruction with higher rendering quality and
substantially less optimization time than NeRF.
