Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors
This is the code of Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors, a paper at CVPR 2023.
If you find this project useful in your research, please consider citing:
@inproceedings{NeuralTPS,
author = {Chao Chen and Zhizhong Han and Yu-Shen Liu},
title = {Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2023},
}
Create virtual environment:
python -m venv neuraltps_venv
source neuraltps_venv/bin/activate
Install dependencies:
pip install -r requirements.txt
Next, for evaluation of the models, complie the extension modules, which are provided by Occupancy Networks. run:
python setup.py build_ext --inplace
To compile the dmc extension, you have to have a cuda enabled device set up. If you experience any errors, you can simply comment out the dmc_* dependencies in setup.py. You should then also comment out the dmc imports in im2mesh/config.py.
Finally, for calculating chamfer distance faster during training, we use the Customized TF Operator nn_distance, run:
cd nn_distance
./tf_nndistance_compile.sh
You can download our preprocessed ShapeNet dataset. Put all folders in data.
You can also preprocess your own dataset by sample.sh, run:
./sample.sh
Training and evaluating single 3d object:
./run.sh