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NeUDF

We present an open surface reconstruction method using UDF, called NeUDF, for reconstructing objects with high-fidelity open surfaces from 2D image inputs.

Project page | Paper

This is the official repo for the implementation of NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering (CVPR 2023).

Usage

Setup environment

Installing the requirements using:

pip install -r requirements.txt

To compile MeshUDF to extract open mesh from the learned UDF field, please run:

cd custom_mc
python setup.py build_ext --inplace
cd ..

To use PyMeshLab and a customized Screened poisson to extract open mesh from the learned UDF field, please run:

pip install pymeshlab

To build PyMeshLab from source, please refer to PyMeshLab.

Running

  • Train without mask
python exp_runner.py --mode train --conf ./confs/womask_open.conf --case <case_name>
  • Train with mask
python exp_runner.py --mode train --conf ./confs/wmask_open.conf --case <case_name>
  • Extract surface using MeshUDF
python exp_runner.py --mode validate_mesh_udf --conf <config_file> --case <case_name> --is_continue

The corresponding mesh can be found in exp/<case_name>/<exp_name>/meshes/mu<iter_steps>.ply.

  • Extract surface using Screened Poisson
python exp_runner.py --mode validate_mesh_spsr --conf <config_file> --case <case_name> --is_continue

The corresponding mesh can be found in exp/<case_name>/<exp_name>/meshes/spsr<iter_steps>.ply.

  • Extract surface using MarchingCubes
python exp_runner.py --mode validate_mesh --conf <config_file> --case <case_name> --is_continue

The corresponding mesh can be found in exp/<case_name>/<exp_name>/meshes/<iter_steps>.ply.

Evaluation using pretrained models

  • Without mask setting
python exp_runner.py --mode validate_mesh_udf --conf ./confs/womask_open.conf --case my_rack_test --is_continue --resolution 512
  • With mask setting
python exp_runner.py --mode validate_mesh_udf --conf ./confs/wmask_open.conf --case my_whale_test --is_continue --resolution 512

Datasets and results

You can download the full datasets and results here and put them in ./public_data/ and ./exp/, respectively.

The data is organized as follows:

public_data
|-- <case_name>
    |-- cameras_xxx.npz
    |-- image
        |-- 000.png
        |-- 001.png
        ...
    |-- mask
        |-- 000.png
        |-- 001.png
        ...
exp
|-- <case_name>
    |-- <conf_name>
        |-- checkpoints
            |-- ckpt_400000.pth

Train NeUDF with custom data

Please refer to the Data Conversion in NeuS.

Citation

Cite as below if you find this repository is helpful to your project:

@inproceedings{Liu23NeUDF,
    author = {Liu, Yu-Tao and Wang, Li and Yang, Jie and Chen, Weikai and Meng, Xiaoxu and Yang, Bo and Gao, Lin},
    title = {NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering},
    booktitle={Computer Vision and Pattern Recognition (CVPR)},
    year = {2023},
}

Acknowledgement

Some code snippets are borrowed from NeuS, NDF and MeshUDF. Thanks for these great projects.