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Campus3D：A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene

The repository contains the utilization and implementation of this ACM MM 2020 Paper. The Campus3D dataset including full version and reduced version can be donwloaded from our website (https://3d.dataset.site is currently unstable, please use https://3d.nus.app). A reproduce work can be found in reproduce-campus3D, which reimplemented the hierarchical learning in PyTorch with more deep models. More details of the reproduce work are presented in the ACM MM 2021 companion paper.

Running Environment

Python packages

The repo has been tested on Python 2.7.17 and Python 3.7.3. To implementation the model of Pointnet2, Python 2.7.17 and tensorflow 1.13 are required.

Package Version
numpy 1.16.6
numba 0.47.0
open3d 0.9.0.0
pyyaml 5.1.2
sparse 0.6.0
h5py 2.10.0
torch 1.0.0
faiss-gpu 1.6.3
tensorflow-gpu 1.13.1

faiss-gpu is optional for GPU KNN search.

Pointnet2 Compile

To run the hierarchical model, one has to comile the tensorflow operations of pointnet2 (models/sem_seg/pointnet2/tf_op). The compiled *.so files in this repo was based on CUDA 10.0 and above python packages.

Docker

To deploy the environment easily, docker file in docker is provided. Below is the example of using the docker.

First build the docker image:

cd docker
docker build -t shinkeli/campus3d:latest .

Run the docker (version>=19.03 with nvidia-container-toolkit) in bash:

docker run -it --gpus all -v <path_to_Campus3D>:/root/Campus3D shinkeli/campus3d:latest /bin/bash

Training and Evaluation

Download the reduced version of Campus3D and place them into data. The data folder should be in the following structure:

├── data
│   ├── data_list.yaml
|   ├── matrix_file_list.yaml
|   └── h_matrices
|       └──lX.csv
|       └── ...
│   └── <area_name_1>
│       └── <area_name_1.pcd
|       └──  <area_name_1>labeX.npy
|       └──  <area_name_1>labeY.npy
|       └──  ...
│   └── <area_name_2>
│       └── <area_name_2>.pcd
|       └── <area_name_2>labeX.npy
|       └── <area_name_2>labeY.npy
|       └──  ...
│   └── ...

Each folder with <area_name> contains the point cloud and label data of one area. The h_matrices folders contains the hierarchical linear relationship between the label in one level and the bottom level. For other structure of data, one can modify data config file data_list.yaml to set customized path. In addition, the train/val/test split can be reset by the data config file.

For the setting of sampling and model, each folder in configs contains one version of setting. The default config folder is configs/sem_seg_default_block, and there are captions for arguments in the config file of this folder.

To apply training of the model:

cd Campus3D
python engine/train.py -cfg <config_dir>

The default <config_dir> is configs/sem_seg_default_block. The model will be saved in log/<dir_name>, where the <dir_name> is the set "OUTPUT_DIR" in the config file.

To apply evaluation of the model on the test set:

cd Campus3D
python engine/eval.py -cfg  <config_dir> -s TEST_SET -ckpt <check_point_name> -o <output_log> -gpu <gpu_id>

The <check_point_name> is the name of ckpt in log/<dir_name>, where the <dir_name> is the set "OUTPUT_DIR" in the config file. The result of IoU, Overall Accuracy and Consistency Rate wiil be written into <output_log>, for which the default name depends on the datetime. <gpu_id> is to set the gpu id for 'faiss' implementation of GPU based nearest neighbour search.

If you find our work useful, please considering citing

@inproceedings{li2020campus3d,
  title={Campus3d: A photogrammetry point cloud benchmark for hierarchical understanding of outdoor scene},
  author={Li, Xinke and Li, Chongshou and Tong, Zekun and Lim, Andrew and Yuan, Junsong and Wu, Yuwei and Tang, Jing and Huang, Raymond},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={238--246},
  year={2020}
}

@inproceedings{liao2021reproducibility,
  title={Reproducibility Companion Paper: Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding},
  author={Liao, Yuqing and Li, Xinke and Tong, Zekun and Zhao, Yabang and Lim, Andrew and Kuang, Zhenzhong and Midoglu, Cise},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={3610--3614},
  year={2021}
}