This is the official implementation of the paper PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images

PDRF

Method Overview

Progressively Deblurring Radiance Field (PDRF) is a novel approach to efficiently reconstruct high quality radiance fields from blurry images. Compared to previous methods like NeRF and DeblurNeRF, PDRF is both much faster and more performant by utilizing radiance field features to model blur.

Quick Start

1. Install environment

pip install -r requirements.txt

2. Download dataset

We evaluate our results based on the dataset captured by DeblurNeRF. You can download the data in here.

3. Setting parameters in configs/

This codebase supports several configurations for radiance field modeling. Two parameters worth noting are args.mode={c2f,nerf} and args.kernel_type={none,DSK,PBE}. Specifically, args.mode=c2f leads to our coarse-to-fine rendering architectures (CRR+FVR), which leverages explicit representation (implemented based on TensoRF) and an improved importance sampling scheme; args.mode=PBE is our two-stage blur-modeling design. DeblurNeRF can be implemented with args.mode=nerf,args.kernel_type=DSK; you can also try any other combinations.

4. Execute

python3 run_nerf.py --config configs/defocustanabata/tx_defocustanabata_full.txt

5. Visualize

To render a video on the learned scene:

python3 run_nerf.py --config configs/defocustanabata/tx_defocustanabata_full.txt --render_only

To render images on the learned scene:

python3 run_nerf.py --config configs/defocustanabata/tx_defocustanabata_full.txt --render_only --render_test

Citation

If you find this useful, please consider citing our paper:

@misc{peng2023pdrf,
    title={PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene
Reconstruction from Blurry Images},
    author={Cheng, Peng and Rama, Chellappa},
    year={2023},
    booktitle = {The 37th AAAI Conference on Artificial Intelligence}
}

Acknowledge

This source code is derived from multiple sources, in particular: nerf-pytorch, Deblurnerf, TensoRF, and HashNeRF-pytorch. We thank the previous authors for their awesome and consistent implementations.