This repository contains the official code implementation for SDS-Complete (NeurIPS 2023). The code is based on https://github.com/ashawkey/stable-dreamfusion.
The code is compatible with Python 3.7 and pytoch 1.13.1. We recommned using anaconda and pip to install the required packages:
conda create -n "sdscomplete" python=3.7 conda activate sdscomplete pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117 pip install scipy pip install tqdm pip install imageio pip install pandas pip install scikit-image==0.18.3 pip install opencv-python pip install matplotlib pip install trimesh pip install transformers pip install diffusers The code assumes that the folder data_processing/redwood_dataset contains the input scans (and GT surfaces for evaluation if available).
. ├── main.py ├── ... ├── data_processing │ ├── README_data.md | ├── ... │ └── redwood_dataset | ├── depths | ├── GT | ├── point_clouds | └── world_planes └── workspace See data_processing/README_data.md for data processing instructions.
python main.py --object_id_number=09639 A running folder with checkpoints, surfaces and rendering images will be logged to the folder workspace
If you find our work useful in your research, please consider citing:
@article{kasten2024point, title={Point Cloud Completion with Pretrained Text-to-Image Diffusion Models}, author={Kasten, Yoni and Rahamim, Ohad and Chechik, Gal}, journal={Advances in Neural Information Processing Systems}, volume={36}, year={2024} }