# Point-GNN **Repository Path**: AI52CV/Point-GNN ## Basic Information - **Project Name**: Point-GNN - **Description**: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud, CVPR 2020. http://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Point-GNN_Graph_Neural_Network_for_3D_Object_Detection_in_a_CVPR_2020_paper.pdf 代码原地址：https://github.com/WeijingShi/Point-GNN - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 3 - **Forks**: 1 - **Created**: 2021-04-06 - **Last Updated**: 2021-11-09 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Point-GNN This repository contains a reference implementation of our [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](http://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Point-GNN_Graph_Neural_Network_for_3D_Object_Detection_in_a_CVPR_2020_paper.pdf), CVPR 2020. If you find this code useful in your research, please consider citing our work: ``` @InProceedings{Point-GNN, author = {Shi, Weijing and Rajkumar, Ragunathan (Raj)}, title = {Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020} } ``` ## Getting Started ### Prerequisites We use Tensorflow 1.15 for this implementation. Please [install CUDA](https://developer.nvidia.com/cuda-10.0-download-archive) if you want GPU support. ``` pip3 install --user tensorflow-gpu==1.15.0 ``` To install other dependencies: ``` pip3 install --user opencv-python pip3 install --user open3d-python==0.7.0.0 pip3 install --user scikit-learn pip3 install --user tqdm pip3 install --user shapely ``` ### KITTI Dataset We use the KITTI 3D Object Detection dataset. Please download the dataset from the KITTI website and also download the 3DOP train/val split [here](https://xiaozhichen.github.io/files/mv3d/imagesets.tar.gz). We provide extra split files for seperated classes in [splits/](splits). We recommand the following file structure: DATASET_ROOT_DIR ├── image # Left color images │ ├── training | | └── image_2 │ └── testing | └── image_2 ├── velodyne # Velodyne point cloud files │ ├── training | | └── velodyne │ └── testing | └── velodyne ├── calib # Calibration files │ ├── training | | └──calib │ └── testing | └── calib ├── labels # Training labels │ └── training | └── label_2 └── 3DOP_splits # split files. ├── train.txt ├── train_car.txt └── ... ### Download Point-GNN Clone the repository recursively: ``` git clone https://github.com/WeijingShi/Point-GNN.git --recursive ``` ## Inference ### Run a checkpoint Test on the validation split: ``` python3 run.py checkpoints/car_auto_T3_train/ --dataset_root_dir DATASET_ROOT_DIR --output_dir DIR_TO_SAVE_RESULTS ``` Test on the test dataset: ``` python3 run.py checkpoints/car_auto_T3_trainval/ --test --dataset_root_dir DATASET_ROOT_DIR --output_dir DIR_TO_SAVE_RESULTS ``` ``` usage: run.py [-h] [-l LEVEL] [--test] [--no-box-merge] [--no-box-score] [--dataset_root_dir DATASET_ROOT_DIR] [--dataset_split_file DATASET_SPLIT_FILE] [--output_dir OUTPUT_DIR] checkpoint_path Point-GNN inference on KITTI positional arguments: checkpoint_path Path to checkpoint optional arguments: -h, --help show this help message and exit -l LEVEL, --level LEVEL Visualization level, 0 to disable,1 to nonblocking visualization, 2 to block.Default=0 --test Enable test model --no-box-merge Disable box merge. --no-box-score Disable box score. --dataset_root_dir DATASET_ROOT_DIR Path to KITTI dataset. Default="../dataset/kitti/" --dataset_split_file DATASET_SPLIT_FILE Path to KITTI dataset split file.Default="DATASET_ROOT_DIR/3DOP_splits/val.txt" --output_dir OUTPUT_DIR Path to save the detection resultsDefault="CHECKPOINT_PATH/eval/" ``` ### Performance Install kitti_native_evaluation offline evaluation: ``` cd kitti_native_evaluation cmake ./ make ``` Evaluate output results on the validation split: ``` evaluate_object_offline DATASET_ROOT_DIR/labels/training/label_2/ DIR_TO_SAVE_RESULTS ``` ## Training We put training parameters in a train_config file. To start training, we need both the train_config and config. ``` usage: train.py [-h] [--dataset_root_dir DATASET_ROOT_DIR] [--dataset_split_file DATASET_SPLIT_FILE] train_config_path config_path Training of PointGNN positional arguments: train_config_path Path to train_config config_path Path to config optional arguments: -h, --help show this help message and exit --dataset_root_dir DATASET_ROOT_DIR Path to KITTI dataset. Default="../dataset/kitti/" --dataset_split_file DATASET_SPLIT_FILE Path to KITTI dataset split file.Default="DATASET_ROOT _DIR/3DOP_splits/train_config["train_dataset"]" ``` For example: ``` python3 train.py configs/car_auto_T3_train_train_config configs/car_auto_T3_train_config ``` We strongly recommand readers to view the train_config before starting the training. Some common parameters which you might want to change first: ``` train_dir The directory where checkpoints and logs are stored. train_dataset The dataset split file for training. NUM_GPU The number of GPUs to use. We used two GPUs for the reference model. If you want to use a single GPU, you might also need to reduce the batch size by half to save GPU memory. Similarly, you might want to increase the batch size if you want to utilize more GPUs. Check the train.py for details. ``` We also provide an evaluation script to evaluate the checkpoints periodically. For example: ``` python3 eval.py configs/car_auto_T3_train_eval_config ``` You can use tensorboard to view the training and evaluation status. ``` tensorboard --logdir=./train_dir ``` ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details