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git clone https://github.com/cong-yi/DualMesh-UDF.git
cd DualMesh-UDF
conda create -n dmudf python=3.9
conda activate dmudf
pip install .
Or you can install it directly by:
pip install "git+https://github.com/cong-yi/DualMesh-UDF"
from DualMeshUDF import extract_mesh
# udf_func: function to evaluate UDF values
# udf_grad_func: function to evaluate UDF values and the gradient
mesh_v, mesh_f = extract_mesh(udf_func, udf_grad_func)Please note that our implementation is not tied to any specific machine learning framework.
To make it more convenient for PyTorch users, we provide an example with several checkpoints for testing and demonstration. Please install PyTorch accordingly and download the checkpoints. Then run the following command:
python example/test.py --pretrained [path_to_checkpoint] --mesh_prefix [folder_prefix_for_mesh]
The default value for mesh_prefix is set to example/results. So the output meshes are stored in the folder example/results.
For example, given the checkpoints in the folder example/checkpoints/ and run:
python example/test.py --pretrained example/checkpoints/fandisk.pth
For other PyTorch-based network, we offer a set of useful tools in example/neural_utils.py for reference. With these tools, you can call our method as follows:
mesh_v, mesh_f = extract_mesh_from_udf(net, device)If you find our method useful for your research, please cite our paper:
@InProceedings{zhang2023dualmeshudf,
author = {Zhang, Congyi and
Lin, Guying and
Yang, Lei and
Li, Xin and
Komura, Taku and
Schaefer, Scott and
Keyser, John and
Wang, Wenping},
title = {Surface Extraction from Neural Unsigned Distance Fields},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {0000-0000}
}
The octree structure is partially built upon the implementation of Dual Contouring of Hermite Data.