GPF: Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation
Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation
Jiaxu Wang, Ziyi Zhang, Renjing Xu*
ICLR 2024
If you found this project useful, please cite us in your paper, this is the greatest support for us.
git clone https://github.com/Mercerai/GPF.git
cd GPF
pip install -r requirements.txt
Dependencies
- torch==1.7.1
- numpy==1.19.2
- CUDA 11.4 or later version
We reorganize the original datasets in our own format. Here we provide a demonstration of the test set of DTU, which can be downloaded here. After placing the demo data into the data directory, one can directly run the test code as follows. In the data_preprocess dir, we provide the code to reorganize the original datasets into our format.
We provide the pretrained model which can be applied to the DTU and BlendedMVS datasets.
python test.py --config ./configs/dtu_config --render_path --num_views 15 --interp_0 14 --interp_1 16 --scan_num 114
python test.py --scan_num 114 --view_num 36
--render_path switch on if camera path is rendered
--num_views how many frames will be rendered on the path
--interp_0 and 1 frames between which need to be interpolated
--scan_num the scan number
--view_num specific view number
The results will be saved in ./log/dtu_eval/
In this repository, we have used codes or datasets from the following repositories. We thank all the authors for sharing great codes or datasets.
@inproceedings{jiaxu2023learning,
title={Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation},
author={Jiaxu, WANG and Zhang, Ziyi and Xu, Renjing},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}