This is Pytorch implementation of the paper on Shape Generation Using Spatially Partitioned Point Clouds.
- Python 3.6
- torch==1.0.1
- torchvision==0.2.2
- open3d
- numpy
- tqdm
- An initial dataset of 5000 points clouds is taken from ShapeNet meshes representing chairs.
- Each point cloud contains 1000 points and they are sampled using Poisson disk sampling.
- Each point cloud is centered at the origin, and the bounding box is normalized, so that the longest dimension spans [-0.5, 0.5].
- After getting the data, store it in a folder called 'shapenet-chairs-pcd'
- Run the pre-process file to order them using kd-tree, then get the bsis using pca and do itertive point order refinement.
python3 pre_process.py
- This will store the true coefficents of pca for the next step.
To train the model, run the following command:
python3 train.py
Note: The argument paramemters have been set to their default values as mentioned in the paper.
For inference run the file:
python3 infer.py --model_path ./models/exp1/weights_50
The initial point cloud after coloring them by index obtained by arraning in order using kd-tree is as shown below:
The resulting meshes after pca on true ground truth distrubtion is:
The new meshes generated using coefficients from GAN are as shown:
