Date : 27th Feb, 2022
This contains the implementation of the paper CodeNeRF. Please refer to the project webpage for demos.
conda env create -f environment.yml
conda activate code_nerf
- Training
- Optimizing with GT pose
- Editing Shapes/Textures
- Pose Optimizing
For ShapeNet-SRN dataset, you can download it from https://drive.google.com/drive/folders/1PsT3uKwqHHD2bEEHkIXB99AlIjtmrEiR
python train.py --gpu <gpu_id> --save_dir <save_dir> --jsonfiles <jsonfile.json> --iters_crop 1000000 --iters_all 1200000
JSON files contain hyper-parameters as well as data directory. 'iters_crop' and 'iters_all' are number of iterations for both cropped and whole images.
python optimize.py --gpu <gpu_id> --saved_dir <trained_dir>
The result will be stored in <trained_dir/test(_num)>, and each folder contains the progress of optimization, and the evaluation of test set. The final optimized results and the quantitative evaluations are stored in 'trained_dir/test(_num)/codes.pth'
@inproceedings{jang2021codenerf,
title={Codenerf: Disentangled neural radiance fields for object categories},
author={Jang, Wonbong and Agapito, Lourdes},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={12949--12958},
year={2021}
}
Some parts of code are borrowed from below amazing repositories.
- The GitHub repository of Pixel NeRF : https://github.com/sxyu/pixel-nerf
- nerf_pl : https://github.com/kwea123/nerf_pl
- NeRF-pytorch : https://github.com/yenchenlin/nerf-pytorch
03951-supp.mp4
MIT