This is the source code for our paper .

This is the source code for our paper Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification

Paper link: https://www.sciencedirect.com/science/article/pii/S0924271620300381

The architecture of our proposed model is as follows

In our experiment, All the codes are tested in Python3.5 (If you use Python 2.7, please add some system paths), CUDA 8.0 and CUDNN 5.1.

Install TensorFlow (We use v1.4.1). Install other python libraries like h5py Compile TF operator (Similar to PointNet++). Firstly, you should find Tensorflow include path and library paths. import tensorflow as tf # include path print(tf.sysconfig.get_include()) # library path print(tf.sysconfig.get_lib()) Then, change the path in all the complie file, like tf_utils/tf_ops/sampling/tf_sampling_compile.sh Finally, compile the source file, we use tf_sampling as example.

cd tf_utils/tf_ops/sampling
chmod +x tf_sampling_compile.sh
./tf_sampling_compile.sh

• Clone this repo

git clone https://github.com/lixiang-ucas/D-FCN.git

• Download the ISPRS Vaihinge dataset from (http://www2.isprs.org/commissions/comm3/wg4/3d-semantic-labeling.html)
• Download the IEEE GRSS Data Fusion Contest 2018 dataset from (http://www.grss-ieee.org/community/technical-committees/data-fusion/2018-ieee-grss-data-fusion-contest/)

python train.py

This code is heavily borrowed from PointSIFT

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

@article{wen2020directionally, title={Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification}, author={Wen, Congcong and Yang, Lina and Li, Xiang and Peng, Ling and Chi, Tianhe}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={162}, pages={50--62}, year={2020}, publisher={Elsevier} }