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PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

Created by Min Zhang, Yifan Wang, Pranav Kadam, Shan Liu, C.-C. Jay Kuo from University of Southern California.

Introduction

This work is an official implementation of our arXiv tech report. We improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. The resulting method is called PointHop++. The first improvement is essential for wearable and mobile computing while the second improvement bridges statistics-based and optimization-based machine learning methodologies. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.

In this repository, we release code and data for training a PointHop++ classification network on point clouds sampled from 3D shapes.

Spark version

This implementation has a high requirement for memory. If you only have 16/32GB memory, please use our new distributed version which is built upon Apache Spark. The new version implements the baseline within 40 minutes, using less than 14GB memory.

Citation

If you find our work useful in your research, please consider citing:

@article{zhang2020pointhop++,
  title={PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification},
  author={Zhang, Min and Wang, Yifan and Kadam, Pranav and Liu, Shan and Kuo, C-C Jay},
  journal={arXiv preprint arXiv:2002.03281},
  year={2020}
}

Installation

The code has been tested with Python 3.5. You may need to install h5py, pytorch, sklearn, pickle and threading packages.

To install h5py for Python:

sudo apt-get install libhdf5-dev
sudo pip install h5py

Usage

To train a single model without feature selection and ensemble to classify point clouds sampled from 3D shapes:

python3 train.py

After the above training, we can evaluate the single model. You can also use the provided model params_single_wo_fe to do evaluation directly.

python3 evaluate.py

Log files and network parameters will be saved to log folder. If you would like to achieve better performance, you can change the argument feature_selection from None to 0.95 or ensemble from False to True or both in train.py and evaluate.py respectively. Or use the provided model params_single_w_fe and params_ensemble_w_fe.

Point clouds of <a href="http://modelnet.cs.princeton.edu/" target="_blank">ModelNet40</a> models in HDF5 files will be automatically downloaded (416MB) to the data folder. Each point cloud contains 2048 points uniformly sampled from a shape surface. Each cloud is zero-mean and normalized into an unit sphere. There are also text files in data/modelnet40_ply_hdf5_2048 specifying the ids of shapes in h5 files.