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