DCC-DIF

This is an implementation of the CVPR 2022 paper Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds.

[Paper] [Supplement] [Data] [Project page] [Jittor Code]

Data

We use the ShapeNet dataset in our experiments. To run our method, the sampled points and their signed distances are needed as training data. We put the code and documents about data processing in sample_SDF_points.zip. For ease of use, we also provide the processed data of bench in 02828884_sdf_samples.zip.

Running code

First, install python denpendencies:

pip install -r requirements.txt

Then, prepare your configuration file based on the example we provide in configs/bench.py. You may need to specify the paths to data and split files.

Now you can reproduce the experimental results in our paper by running:

python train.py configs.bench
python reconstruct.py configs.bench
python evaluate.py configs.bench

The pretrained models can be found here, we also provide demo code and document to use pretrained models in usage_demo.zip.

Citing DCC-DIF

If you find this code useful, please consider citing:

@inproceedings{Li2022DCCDIF,
    title={Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds},
    author={Tianyang Li and Xin Wen and Yu-Shen Liu and Hua Su and Zhizhong Han},
    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2022}
}