108 lines
2.8 KiB
Markdown
108 lines
2.8 KiB
Markdown
# Shape Generation and Completion Through Point-Voxel Diffusion
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<center>
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<img src="assets/pvd_teaser.gif" width="80%"/>
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</center>
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[Project](https://alexzhou907.github.io/pvd) | [Paper](https://arxiv.org/abs/2104.03670)
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Implementation of Shape Generation and Completion Through Point-Voxel Diffusion
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[Linqi Zhou](https://alexzhou907.github.io), [Yilun Du](https://yilundu.github.io/), [Jiajun Wu](https://jiajunwu.com/)
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## Requirements:
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Make sure the following environments are installed.
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```
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python==3.6
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pytorch==1.4.0
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torchvision==0.5.0
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cudatoolkit==10.1
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matplotlib==2.2.5
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tqdm==4.32.1
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open3d==0.9.0
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trimesh=3.7.12
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scipy==1.5.1
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```
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Install PyTorchEMD by
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```
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cd metrics/PyTorchEMD
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python setup.py install
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cp build/**/emd_cuda.cpython-36m-x86_64-linux-gnu.so .
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```
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The code was tested on Unbuntu with Titan RTX.
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## Data
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For generation, we use ShapeNet point cloud, which can be downloaded [here](https://github.com/stevenygd/PointFlow).
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For completion, we use ShapeNet rendering provided by [GenRe](https://github.com/xiumingzhang/GenRe-ShapeHD).
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We provide script `convert_cam_params.py` to process the provided data.
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For training the model on shape completion, we need camera parameters for each view
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which are not directly available. To obtain these, simply run
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```bash
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$ python convert_cam_params.py --dataroot DATA_DIR --mitsuba_xml_root XML_DIR
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```
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which will create `..._cam_params.npz` in each provided data folder for each view.
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## Pretrained models
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Pretrained models can be downloaded [here](https://drive.google.com/drive/folders/1Q7aSaTr6lqmo8qx80nIm1j28mOHAHGiM?usp=sharing).
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## Training:
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```bash
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$ python train_generation.py --category car|chair|airplane
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```
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Please refer to the python file for optimal training parameters.
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## Testing:
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```bash
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$ python train_generation.py --category car|chair|airplane --model MODEL_PATH
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```
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## Results
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Some generation and completion results are as follows.
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<center>
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<img src="assets/gen_comp.gif" width="60%"/>
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</center>
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Multimodal completion on ShapeNet chair.
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<center>
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<img src="assets/mm_shapenet.gif" width="80%"/>
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</center>
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Multimodal completion on PartNet.
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<center>
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<img src="assets/mm_partnet.gif" width="80%"/>
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</center>
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Multimodal completion on RedWood 3DScan Chairs.
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<center>
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<img src="assets/mm_redwood.gif" width="80%"/>
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</center>
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## Reference
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```
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@inproceedings{Zhou_2021_ICCV,
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author = {Zhou, Linqi and Du, Yilun and Wu, Jiajun},
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title = {3D Shape Generation and Completion Through Point-Voxel Diffusion},
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booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
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month = {October},
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year = {2021},
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pages = {5826-5835}
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}
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```
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## Acknowledgement
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For any questions related to codes and experiment setting, please contact [Linqi Zhou](linqizhou@stanford.edu) and [Yilun Du](yilundu@mit.edu).
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