Painted-Point-GNN

This repository is contains a modified implementation of the paper Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud, CVPR 2020.

Getting Started

Prerequisites

We use Tensorflow 1.15 for this implementation. Please install CUDA 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. We provide extra split files for seperated classes in 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            
|   |   └── painted_lidar   # Contains painted point clouds            
│   └── 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/k-alkiek/Painted-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