EC-Net: an Edge-aware Point set Consolidation Network

by Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng. Details are in project page.

Introduction

This repository is for our ECCV 2018 paper 'EC-Net: an Edge-aware Point set Consolidation Network'. This project is based on our previous project PU-Net.

Installation

This repository is based on Tensorflow and the TF operators from PointNet++. Therefore, you need to install tensorflow and compile the TF operators.

For installing tensorflow, please follow the official instructions in here. The code is tested under TF1.3 (higher version should also work) and Python 2.7 on Ubuntu 16.04.

For compiling TF operators, please check tf_xxx_compile.sh under each op subfolder in code/tf_ops folder. Note that you need to update nvcc, python and tensoflow include library if necessary. You also need to remove -D_GLIBCXX_USE_CXX11_ABI=0 flag in g++ command in order to compile correctly if necessary.

To compile the operators in TF version >=1.4, you need to modify the compile scripts slightly.

First, find Tensorflow include and library paths.

    TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
    TF_LIB=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_lib())')

Then, add flags of -I$TF_INC/external/nsync/public -L$TF_LIB -ltensorflow_framework to the g++ commands.

We adopt the Dijkstra algorithm implemented in python-graph library, you can follow the instruction in here to install it.

Usage

1. Clone the repository:

   git clone https://github.com/yulequan/EC-Net.git
   cd EC-Net

2. Compile the TF operators:

   Follow the above information to compile the TF operators.

3. Train the model:

   cd code
   python main.py --phase train --gpu 0 --log_dir ../model/myownmodel

4. Evaluate the model:

   We provide the pretrained model in folder model/pretrain. To evaluate the model, you need to put the test point clouds (in .xyz format) in the folder eval_input.

   Then run:

   cd code
   python main.py --phase test --log_dir ../model/pretrain

   You will see the input point cloud, output point cloud, and the identified edge points in the folder eval_result.

Citation

If EC-Net is useful for your research, please consider citing:

@inproceedings{yu2018ec,
     title={EC-Net: an Edge-aware Point set Consolidation Network},
     author={Yu, Lequan and Li, Xianzhi and Fu, Chi-Wing and Cohen-Or, Daniel and Heng, Pheng-Ann},
     booktitle = {ECCV},
     year = {2018}

}

Related project

1. PU-Net
2. PointNet++

Questions

Please contact 'lqyu@cse.cuhk.edu.hk'