Implementation of AAAI'24 paper NeuSurf: On-Surface Priors for Neural Surface Reconstruction from Sparse Input Views
Project Page | Paper | Data | Mesh Results
Our code is implemented in Python 3.10, PyTorch 2.0.0 and CUDA 11.7.
- Install Python dependencies
conda create -n neusurf python=3.10
conda activate neusurf
pip install torch==2.0.0 torchvision==0.15.1
pip install -r requirements.txt
- Compile C++ extensions
cd extensions/chamfer_dist
python setup.py install
Data structure:
data
|-- DTU_pixelnerf
|-- <case_name, e.g. dtu_scan24>
|-- cameras_sphere.npz
|-- pcd
|-- <case_name>.ply
|-- cam4feat
|-- pair.txt
|-- cam_00000000_flow3.txt
|-- cam_00000001_flow3.txt
...
|-- image
|-- 000000.png
|-- 000001.png
...
|-- mask
|-- 000.png
|-- 001.png
...
|-- DTU_sparseneus
|-- blendedmvs_sparse
You can directly download the processed data here.
- Training
CUDA_VISIBLE_DEVICES=0
python exp_runner.py --mode train --conf ./confs/dtu.conf --case <case_name, e.g. dtu_scan24>
- Extract mesh
CUDA_VISIBLE_DEVICES=0
python exp_runner.py --mode validate_mesh --conf ./confs/dtu.conf --case <case_name> --is_continue
If you find our work useful in your research, please consider citing:
@inproceedings{huang2024neusurf,
title={NeuSurf: On-Surface Priors for Neural Surface Reconstruction from Sparse Input Views},
author={Huang, Han and Wu, Yulun and Zhou, Junsheng and Gao, Ge and Gu, Ming and Liu, Yu-Shen},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={3},
pages={2312--2320},
year={2024}
}
This implementation is based on CAP-UDF, D-NeuS and Vis-MVSNet. Thanks for these great works.
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