FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without
  Learned Priors
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.
67172185a9bd6e5e5b5c3ec8
Chin-Yang Lin
admin_assigned
2024-10-22T09:06:04.512Z
66b9d0996f861799b80b457a
/avatars/31d4989c5e0983283a6a8e8a152b82e6.svg
CY Lin
user
chinyanglin
67172185a9bd6e5e5b5c3ec9
Chung-Ho Wu
admin_assigned
2024-10-22T09:07:01.828Z
65d70288ca16ef9ba7f72542
/avatars/8ceec128b7e7be6d1b4c615b9eced98d.svg
Chung-Ho Wu
user
kkennethwu
67172185a9bd6e5e5b5c3eca
Chang-Han Yeh
67172185a9bd6e5e5b5c3ecb
Shih-Han Yen
67172185a9bd6e5e5b5c3ecc
Cheng Sun
67172185a9bd6e5e5b5c3ecd
Yu-Lun Liu
admin_assigned
2024-10-22T09:07:23.744Z
6459d5da3b6fafd9664807ab
/avatars/57430d1bbde3a2fe5586e5fbcafb0e74.svg
Yu-Lun Liu
user
yulunliu
FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without
  Learned Priors
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.
FrugalNeRF addresses few-shot NeRF challenges by using weight-sharing voxels and a cross-scale adaptation scheme, improving quality and reducing training time.
Neural Radiance Fields FrugalNeRF frequency regularization pre-trained priors weight-sharing voxels cross-scale geometric adaptation reprojection errors LLFF DTU RealEstate-10K