A fast and low memory requirement version of PointHop and PointHop++

Created by Min Zhang

Introduction

This work is an improved implementation of our PointHop method and PointHop++ method, which is built upon Apache Spark. With 12 cores (Intel (R) core ™ i7-5930k CPU @ 3.5GHZ), PointHop finishes in 20 minutes using less than 12GB memory, and PointHop++ finishes in 40 minutes using less than 14GB memory.

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

Citation

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

@article{zhang2020pointhop,
  title={PointHop: An Explainable Machine Learning Method for Point Cloud Classification},
  author={Zhang, Min and You, Haoxuan and Kadam, Pranav and Liu, Shan and Kuo, C-C Jay},
  journal={IEEE Transactions on Multimedia},
  year={2020},
  publisher={IEEE}
}

@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 2.7 and 3.5, Java 8.0. You may need to install h5py, sklearn, pickle and pyspark packages.

To check your java version:

java --version

To install pyspark for Python:

sudo pip install pyspark

If you are using Python 3. You may need to set up your configuarion.

PYSPARK_PYTHON=/usr/bin/python3
PYSPARK_DRIVER_PYTHON=/usr/bin/python3

Usage

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

python3 pointhop_spark.py

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

python3 pointhop2_spark.py

Point clouds of ModelNet40 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.