Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors (TPAMI 2024 / CVPR 2023)

Project | paper (TPAMI) | paper (CVPR)

Citation

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},
}

Setup

Installation

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

Dataset

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 Evaluation

Training and evaluating single 3d object:

./run.sh