title: FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without
    Learned Priors
summary: Neural Radiance Fields (NeRF) face significant challenges in few-shot
  scenarios, primarily due to overfitting and long training times for
  high-fidelity rendering. Existing methods, such as FreeNeRF and SparseNeRF, use
  frequency regularization or pre-trained priors but struggle with complex
  scheduling and bias. We introduce FrugalNeRF, a novel few-shot NeRF framework
  that leverages weight-sharing voxels across multiple scales to efficiently
  represent scene details. Our key contribution is a cross-scale geometric
  adaptation scheme that selects pseudo ground truth depth based on reprojection
  errors across scales. This guides training without relying on externally
  learned priors, enabling full utilization of the training data. It can also
  integrate pre-trained priors, enhancing quality without slowing convergence.
  Experiments on LLFF, DTU, and RealEstate-10K show that FrugalNeRF outperforms
  other few-shot NeRF methods while significantly reducing training time, making
  it a practical solution for efficient and accurate 3D scene reconstruction.
paper_authors._id: 67172185a9bd6e5e5b5c3ec8
paper_authors.name: Chin-Yang Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-22T09:06:04.512Z
paper_authors._id: 66b9d0996f861799b80b457a
paper_authors.avatarUrl: /avatars/31d4989c5e0983283a6a8e8a152b82e6.svg
paper_authors.fullname: CY Lin
paper_authors.type: user
paper_authors.user: chinyanglin
paper_authors._id: 67172185a9bd6e5e5b5c3ec9
paper_authors.name: Chung-Ho Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-22T09:07:01.828Z
paper_authors._id: 65d70288ca16ef9ba7f72542
paper_authors.avatarUrl: /avatars/8ceec128b7e7be6d1b4c615b9eced98d.svg
paper_authors.fullname: Chung-Ho Wu
paper_authors.type: user
paper_authors.user: kkennethwu
paper_authors._id: 67172185a9bd6e5e5b5c3eca
paper_authors.name: Chang-Han Yeh
paper_authors._id: 67172185a9bd6e5e5b5c3ecb
paper_authors.name: Shih-Han Yen
paper_authors._id: 67172185a9bd6e5e5b5c3ecc
paper_authors.name: Cheng Sun
paper_authors._id: 67172185a9bd6e5e5b5c3ecd
paper_authors.name: Yu-Lun Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-22T09:07:23.744Z
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: FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without
    Learned Priors
paper_summary: Neural Radiance Fields (NeRF) face significant challenges in few-shot
  scenarios, primarily due to overfitting and long training times for
  high-fidelity rendering. Existing methods, such as FreeNeRF and SparseNeRF, use
  frequency regularization or pre-trained priors but struggle with complex
  scheduling and bias. We introduce FrugalNeRF, a novel few-shot NeRF framework
  that leverages weight-sharing voxels across multiple scales to efficiently
  represent scene details. Our key contribution is a cross-scale geometric
  adaptation scheme that selects pseudo ground truth depth based on reprojection
  errors across scales. This guides training without relying on externally
  learned priors, enabling full utilization of the training data. It can also
  integrate pre-trained priors, enhancing quality without slowing convergence.
  Experiments on LLFF, DTU, and RealEstate-10K show that FrugalNeRF outperforms
  other few-shot NeRF methods while significantly reducing training time, making
  it a practical solution for efficient and accurate 3D scene reconstruction.
paper_ai_summary: FrugalNeRF addresses few-shot NeRF challenges by using weight-sharing voxels and a cross-scale adaptation scheme, improving quality and reducing training time.
paper_ai_keywords: Neural Radiance Fields FrugalNeRF frequency regularization pre-trained priors weight-sharing voxels cross-scale geometric adaptation reprojection errors LLFF DTU RealEstate-10K