Sphere-Guided Training of Neural Implicit Surfaces (CVPR 2023)

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The official implementation of Sphere-Guided Training of Neural Implicit Surfaces. The main code for sphere-guidance is in the spheres directory. We include the integration of our method with three different surface reconstruction methods: NeuS, NeuralWarp, and UNISURF.

Installation

The main dependencies are PyTorch with CUDA and PyTorch3D. We recommend following the official installation instructions from PyTorch3D.

Alternatively, we provide a Python=3.9 anaconda environment with PyTorch=1.13, CUDA=11.6, PyTorch3D=0.7, and additional dependencies including numpy, open3d, opencv_python, pymcubes, pyhocon, tensorboard, trimesh:

conda env create -f environment.yml
conda activate spheregt

Using UNISURF codebase additionally requires compiling some extension modules:

cd unisurf
python setup.py build_ext --inplace

Data

For the DTU dataset, as each method used its own version of the data, we recommend using the indications provided in the integrated repositories. For NeuS, the data has to be downloaded from here and added to the data directory. For NeuralWarp, it can easily be downloaded with:

./NeuralWarp/download_dtu.sh

Similar, for UNISURF:

cd unisurf
./download_dataset.sh

For the Realistic Synthetic 360 (Nerf Synthetic) dataset, we preprocess each scene to fit into the bounding sphere of radius one and compute the source views required for NeuralWarp. The processed data can be downloaded from here and added to the data directory.

For the BlendedMVS dataset, we advise to use the preprocessed data from NeuS. For reconstruction using NeuralWarp, the source views have to be computed with the suggested method in the original repository.

Training

We provide a straightforward way for launching the training of the methods:

./scripts/train.sh METHOD DATASET SCENE EXPNAME [sphere]

• METHOD specifies one of the three methods: neus, nwarp, unisurf.
• DATASET selects one of the datasets downloaded in the previous section: dtu, bmvs, nerf.
• SCENE indicates the training scene from the selected dataset.
• sphere option uses the proposed sphere-guidance training.

Extraction and Evaluation

Using the same parameters as for the training, you can extract the surface from a trained model with:

./scripts/extract.sh METHOD DATASET SCENE EXPNAME [sphere]

We also provide easy-to-use evaluation scripts for DTU and Nerf Synthetic datasets:

./scripts/eval_dtu.sh METHOD SCENE EXPNAME
./scripts/eval_nerf.sh METHOD SCENE EXPNAME

For DTU dataset, the data required for evaluation can be downloaded with:

./NeuralWarp/download_dtu_eval.sh

Citation

Should you find this research useful in your work, please cite:

@InProceedings{Dogaru_2023_CVPR,
  author    = {Dogaru, Andreea and Ardelean, Andrei-Timotei and Ignatyev, Savva and Zakharov, Egor and Burnaev, Evgeny},
  title     = {Sphere-Guided Training of Neural Implicit Surfaces},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {20844-20853}
}

License

Please see the LICENSE.