Multiresolution Tree Networks for 3D Point Cloud Processing (ECCV 2018)

This repository contains the source code for the ECCV 2018 paper Multiresolution Tree Networks for 3D Point Clout Processing.

Project page

Dependencies

• numpy
• pytorch
• tensorboardX
• fxia22/pointGAN (optional - if you want faster Chamfer Distance) Thanks to Fei Xia'a for making the code publicly available. We have a version in this repo already, but might not be up-to-date.

Train

First, you need to change the dataset path in the file train_img2pc.py. We are using the rendered images from https://github.com/chrischoy/3D-R2N2. You will also need a path for the point clouds in .npy format. Finally, you can train a model by using the following command:

python train_img2pc.py --name experiment_name

If you want to run the model, change the folder name indicated in run_img2pc.py and use the following command:

python run_img2pc.py --n experiment_name

Notice thatexperiment_name should match in both cases. Similarly, we also have evaluation code to reproduce the paper's numbers.

Point Sampling

This repository also contains code for sampling the point clouds in the sampler folder. It is a single .java file and it contains a README with specific instructions. This code automatically sorts the points according to a kd-tree structure.

Citation

If you use any part of this code or data, consider citing this work:

@inProceedings{mrt18,
  title={Multiresolution Tree Networks for 3D Point Cloud Processing},
  author = {Matheus Gadelha and Rui Wang and Subhransu Maji},
  booktitle={ECCV},
  year={2018}
}