SDF-Diffusion
Diffusion-Based Signed Distance Fields for 3D Shape Generation (CVPR 2023)
Requirements
- pytorch
- pytorch3d
- h5py
- einops
- scipy
- scikit-image
- tqdm
- point-cloud-utils
Dataset
The preprocessed dataset can be downloaded in Huggingface
The dataset (~13GB for resolution 32, ~50GB for 64) should be unzipped and located like this:
SDF-Diffusion
├── config
├── src
├── ...
├── main.py
data
├── sdf-diffusion
│ ├── sdf.res32.level0.0500.PC15000.pad0.20.hdf5
│ ├── sdf.res64.level0.0313.PC15000.pad0.20.hdf5
To use the dataset, please cite ShapeNet:
@article{chang2015shapenet,
title={Shapenet: An information-rich 3d model repository},
author={Chang, Angel X and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and others},
journal={arXiv preprint arXiv:1512.03012},
year={2015}
}
The dataset can be used only for non-commercial research and educational purpose.
Training
Comming Soon
Inference & Evaluation
Comming Soon
Citation
@inproceedings{shim2023diffusion,
title={Diffusion-Based Signed Distance Fields for 3D Shape Generation},
author={Shim, Jaehyeok and Kang, Changwoo and Joo, Kyungdon},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={20887--20897},
year={2023}
}