This repository contains the official Implementation for "BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives".
Tested on NVIDIA A100 and NVIDIA RTX3090.
- python >= 3.8
- pytorch >= 2.0.1
- tinycudann >= 1.7
- nerfacc >= 0.5.0
- Install the remaining dependencies via
pip install -r requirements.txt
-
Download nerf_synthetic (1.6G) from here.
- 100 train images
- 200 test images
-
Train and run
python baangp/train_baangp.py --scene [lego] --data-root [your_data_root] --save-dir [your_save_dir] --c2f 0.1 0.5
Coming soon.
BAA-NGP code is heavily based on nerfacc and barf.
If you use this code for your research, please cite our paper BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives
@article{liu2023baangp,
title={BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives.},
author={Sainan Liu and Shan Lin and Jingpei Lu and Shreya Saha and Alexey Supikov and Michael Yip},
journal={arXiv preprint arXiv:2306.04166},
year={2023}
}This “research quality code” is for Non-Commercial purposes and provided by Intel “As Is” without any express or implied warranty of any kind. Please see the dataset's applicable license for terms and conditions. Intel does not own the rights to this data set and does not confer any rights to it. Intel does not warrant or assume responsibility for the accuracy or completeness of any information, text, graphics, links or other items within the code. A thorough security review has not been performed on this code. Additionally, this repository may contain components that are out of date or contain known security vulnerabilities.
nerf_synthetic dataset: Please see the dataset's applicable license for terms and conditions. Intel does not own the rights to this data set and does not confer any rights to it.
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