- install the requirements
pip install -r requirements.txt- reset jnerf
cd python
python -m pip install -e .
Two test mode,
- run test.py for all competition datatset at once, and it will generate result image to the folder ./result.
python test.py- run train.py with --config-file and --task test, but it just test one set.
Example:
python train.py --config-file ./projects/ngp/configs/ngp_scar.py --task testExample:
python train.py --config-file ./projects/ngp/configs/ngp_scar.py
After training , the checkpoint file and validated images will be generated to the folder ./logs.
All the comptetition dataset is at the folder ./data/nerf_synthetic, and you can modify it at the config file for each dataset.
(A) dataset download: here
(B) more test dataset: here
-
add reflected radiance model like Ref-NeRF, improving NGP's ability to represent and render the glossy surfaces.
-
add BARF based on jittor framework.
The original implementation comes from the following project:
@article{hu2020jittor,
title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},
author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},
journal={Science China Information Sciences},
volume={63},
number={222103},
pages={1--21},
year={2020}
}
@article{mueller2022instant,
author = {Thomas M\"uller and Alex Evans and Christoph Schied and Alexander Keller},
title = {Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
journal = {ACM Trans. Graph.},
issue_date = {July 2022},
volume = {41},
number = {4},
month = jul,
year = {2022},
pages = {102:1--102:15},
articleno = {102},
numpages = {15},
url = {https://doi.org/10.1145/3528223.3530127},
doi = {10.1145/3528223.3530127},
publisher = {ACM},
address = {New York, NY, USA},
}
@inproceedings{mildenhall2020nerf,
title={NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis},
author={Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng},
year={2020},
booktitle={ECCV},
}
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