This repository aims to implement the ECCV 2018 paper: Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images in PyTorch. The official code in Tensorflow is available online. Based on the proposed structure, we replaced the VGG model by a U-Net based autoencoder to reconstruct the image, which helps the net to converge faster.
- PyTorch 1.0 (Enable Sparse Tensor)
- >= Python 3
- >= Cuda 9.2 (Enable Chamfer Distance)
- Visdom (Enable Data Visualization)
cd ./model/chamfer/
python setup.py install
We use the same dataset as the one used in Pixel2Mesh. The point clouds are from ShapeNet and the rendered views are from 3D-R2N2.
The whole dataset can be downloaded Here.
Please respect the ShapeNet license while using.
python train.py
The hyper-parameters can be changed from command. To get more help, please use
python train.py -h
To evaluate the model on one example, please use the following command
python evaluate.py --dataPath *** --modelPath ***
Due to the device limit, we trained our model on the airplane class instead of the whole dataset. A trained model is provided Here
Some test examples are shown as below:
