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.

faiss-gpu is optional for GPU KNN search.

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.

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

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.