3DBN

This repository contains the public release of the python implementation of 3D Backbone Network for 3D Object Detection,

If you find this code useful, please cite our paper:

@article{DBLP:journals/corr/abs-1901-08373,
  author    = {Xuesong Li and Jos{\'{e}} E. Guivant and Ngaiming Kwok and Yongzhi Xu},
  title     = {3D Backbone Network for 3D Object Detection},
  journal   = {CoRR},
  volume    = {abs/1901.08373},
  year      = {2019},
  url       = {http://arxiv.org/abs/1901.08373},
  archivePrefix = {arXiv},
  eprint    = {1901.08373},
}

Pipeline

Install

Implemented and tested on Ubuntu 16.04 with Python 3.6 and Pytorch 1.0.

1. Clone the repo

git clone https://github.com/Benzlxs/tDBN.git

2. Install Python dependencies The miniconda3 package manager package is recommended.

cd ./tDBN
pip3 install -r requirements.txt

3. Install Pytorch. Visiting pytorch official webpage and installing 1.0 version PyTorch according to your hardware configuration.

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

4. Install SparseConvNet according to its README file.

5. Compile the protos.

cd ./tDBN
bash protos/run_protoc.sh

Dataset

1. Downlaoad the KITTI and arrange files as following:

kitti_dataset
        training
                image_2
                label_2
                calib
                velodyne
                velodyne_reduced
        testing
                image_2
                calib
                velodyne
                velodyne_reduced

2. Split dataset and put them under the folder, ./tDBN/kitti/data_split, we provide the two set of data split, 50/50 split or 75/25 split, or you can customize your data split ratio.

3. Create kitti dataset infos:

cd ./tDBN
python ./scripts/create_data.py create_kitti_info_file --data_path=kitti_dataset

4. Create reduced point cloud:

cd ./tDBN
python ./scripts/create_data.py create_reduced_point_cloud --data_path=kitti_dataset

5. Create groundtruth database:

cd ./tDBN
python ./scripts/create_data.py create_groundtruth_database --data_path=kitti_dataset

6. Modify the directory in config file Go to the config folder and configurate the database_info_path, kitti_info_path and kitti_root_path to your path.

Training

1. Select your config file and output directory in train.sh, like setting config=./configs/car_tDBN_bv_2.config
2. Start to train:

cd ./tDBN
bash train.sh

3. Training results are saved in output directory, check the log.txt and 'eval_log.txt' for detailed performance.
4. Some training results are as following

Eval_at_125571
Car [email protected], 0.70, 0.70
3D  AP 87.98, 77.89, 76.35
Eval_at_160940
Car [email protected], 0.70, 0.70
3D  AP 88.20, 77.59, 75.58

Evaluate and inference

1. Select your config file and output directory in train.sh, like setting config=./configs/car_tDBN_bv_2.config
2. Set the model path that you want to evaluate, ckpt_path
3. If you want to evlaute, set test=False, if you want to generate testing result, set test=True and kitti_info_path=your_kitti_dataset_root/kitti_infos_test.pkl
4. Start to evaluate or inference:

cd ./tDBN
bash evaluator.sh

5. Testing results on KITTI benchmark.

Benchmark	Easy	Moderate	Hard
Car (Detection)	90.30 %	88.62 %	80.08 %
Car (Orientation)	89.93 %	87.95 %	79.32 %
Car (3D Detection)	83.56 %	74.64 %	66.76 %
Car (Bird's Eye View)	88.13 %	79.40 %	77.97 %

Acknowledge

Thanks to the team of Yan Yan , we have benifited a lot from their previous work SECOND.