BoostMVSNeRFs: Boosting MVS-based NeRFs to Generalizable View Synthesis
  in Large-scale Scenes
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/.
669f5545ece12f050c2c531e
Chih-Hai Su
admin_assigned
2024-07-23T08:21:46.751Z
65d61572531de9717708be9f
/avatars/58c30060e847ddaefe3e8f0a88008725.svg
Chih-hai Su
user
ohinic
669f5545ece12f050c2c531f
Chih-Yao Hu
669f5545ece12f050c2c5320
Shr-Ruei Tsai
admin_assigned
2024-07-23T08:22:12.627Z
665c7ff2d94d2b1067b5f304
/avatars/f8af0a212fdccf40beaa0d7399410f3b.svg
Shr-Ruei Tsai
user
RayTsai-030
669f5545ece12f050c2c5321
Jie-Ying Lee
claimed_verified
2024-07-23T07:47:00.139Z
655f1770f74fa124d1172ec1
/avatars/e4413693c34974fac75a438ffe2cc630.svg
Jay Lee
user
jayinnn
669f5545ece12f050c2c5322
Chin-Yang Lin
669f5545ece12f050c2c5323
Yu-Lun Liu
admin_assigned
2024-07-23T08:22:31.645Z
6459d5da3b6fafd9664807ab
/avatars/57430d1bbde3a2fe5586e5fbcafb0e74.svg
Yu-Lun Liu
user
yulunliu
BoostMVSNeRFs: Boosting MVS-based NeRFs to Generalizable View Synthesis
  in Large-scale Scenes
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/.
BoostMVSNeRFs enhances MVS-based NeRFs by improving rendering quality in large-scale scenes through advanced cost volume selection and combination techniques.
Neural Radiance Fields NeRFs MVS-based NeRFs cost volumes volume rendering feed-forward end-to-end trainable large-scale datasets unbounded outdoor scenarios