This is an official implementation of the paper "Neural Invertible Warp for NeRF".
Assuming a fresh Anaconda environment, you can install the dependencies by
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
numpy==1.26.4
pip install -r requirements.txt
conda install -c conda-forge cupy cuda-version=11.6
DTU
-
Images: We use the DTU dataset, produced by SPARF, where the images are processed and resized to 300 x 400. Download the data here.
-
Ground-truth depth maps: For geometry evaluation, we report the depth error. Download the depth maps. They are extracted from MVSNeRF.
LLFF
The LLFF real-world data can be found in the NeRF Google Drive. You can download the dataset by running
gdown 16VnMcF1KJYxN9QId6TClMsZRahHNMW5g # download nerf_llff_data.zip
unzip nerf_llff_data.zip
rm -f nerf_llff_data.zip
mv nerf_llff_data data/llff
The data directory should contain the subdirectories llff and dtu. If you have downloaded the datasets, you can create soft links to them within the data directory.
All checkpoints, logs, and Tensorboard event files are written to ~/output/<GROUP>/<NAME>
by default. Override this with --output_root=/your_path
.
# Train the reference model
bash scripts/train_llff.sh
# Evaluate the released checkpoint
bash scripts/eval_llff.sh
Dataset | Training script | Eval script | Notes |
---|---|---|---|
LLFF | train_llff.sh |
eval_llff.sh |
Matches Table 1 in supplementary paper |
DTU | train_dtu.sh |
eval_dtu.sh |
Matches Table 2 in supplementary paper |
This code is for non-commercial use. If you find our work useful in your research please cite our paper:
@inproceedings{chng2024invertible,
title={Invertible neural warp for nerf},
author={Chng, Shin-Fang and Garg, Ravi and Saratchandran, Hemanth and Lucey, Simon},
booktitle={European Conference on Computer Vision},
pages={405--421},
year={2024},
organization={Springer}
}