  While Neural Radiance Fields (NeRFs) have demonstrated exceptional quality,
their protracted training duration remains a limitation. Generalizable and
MVS-based NeRFs, although capable of mitigating training time, often incur
tradeoffs in quality. This paper presents a novel approach called BoostMVSNeRFs
to enhance the rendering quality of MVS-based NeRFs in large-scale scenes. We
first identify limitations in MVS-based NeRF methods, such as restricted
viewport coverage and artifacts due to limited input views. Then, we address
these limitations by proposing a new method that selects and combines multiple
cost volumes during volume rendering. Our method does not require training and
can adapt to any MVS-based NeRF methods in a feed-forward fashion to improve
rendering quality. Furthermore, our approach is also end-to-end trainable,
allowing fine-tuning on specific scenes. We demonstrate the effectiveness of
our method through experiments on large-scale datasets, showing significant
rendering quality improvements in large-scale scenes and unbounded outdoor
scenarios. We release the source code of BoostMVSNeRFs at
https://su-terry.github.io/BoostMVSNeRFs/.
