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DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection (ICLR 2023)

DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Jinrong Yang, Lin Song, Songtao Liu, Weixin Mao, Zeming Li, Xiaoping Li*, Hongbin Sun*, Jian Sun, Nanning Zheng

[Paper]

Getting Started

Installation

a. Clone this repository

git clone https://github.com/yancie-yjr/DBQ-SSD.git && cd DBQ-SSD

b. Configure the environment

We have tested this project with the following environments:

  • Ubuntu18.04/20.04
  • Python = 3.8.10
  • PyTorch = 1.10.2
  • CUDA = 11.1
  • CMake >= 3.13
  • spconv = 1.0 
    # install spconv=1.0 library
git clone https://github.com/yifanzhang713/spconv1.0.git
cd spconv1.0
sudo apt-get install libboostall-dev
python setup.py bdist_wheel
pip install ./dist/spconv-1.0*   # wheel file name may be different
cd ..

c. Install pcdet toolbox.

pip install -r requirements.txt
python setup.py develop

d. Prepare the datasets.

Download the official KITTI with road planes and Waymo datasets, then organize the unzipped files as follows:

DBQ-SSD
├── data
│   ├── kitti
│   │   ├── ImageSets
│   │   ├── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   ├── testing
│   │   │   ├──calib & velodyne & image_2
│   ├── waymo
│   │   │── ImageSets
│   │   │── raw_data
│   │   │   │── segment-xxxxxxxx.tfrecord
|   |   |   |── ...
|   |   |── waymo_processed_data_v0_5_0
│   │   │   │── segment-xxxxxxxx/
|   |   |   |── ...
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1/
│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_train.pkl (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_val.pkl (optional)
├── pcdet
├── tools

Generate the data infos by running the following commands:

# KITTI dataset
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

# Waymo dataset
python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
    --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml

Quick Inference

We provide the pre-trained weight file so you can just run with that:

cd tools 

# To reduce the pressure on the CPU during preprocessing, a suitable batchsize is recommended, e.g. 16. (Over 5 batches per second on RTX2080Ti)
OMP_NUM_THREADS=1 python tools/test.py --cfg_file tools/cfgs/kitti_models/DBQ-SSD.yaml --batch_size 16 --workers 8 \
    --ckpt DBQ-SSD.pth --set MODEL.POST_PROCESSING.RECALL_MODE 'speed'

Training

The configuration files are in tools/cfgs/kitti_models/DBQ-SSD.yaml, and the training scripts are in tools/scripts.

Train with single or multiple GPUs: (e.g., KITTI dataset)

python tools/train.py --cfg_file tools/cfgs/kitti_models/DBQ-SSD.yaml

# or 

sh tools/scripts/dist_train.sh ${NUM_GPUS} --cfg_file tools/cfgs/kitti_models/DBQ-SSD.yaml

Evaluation

Evaluate with single or multiple GPUs: (e.g., KITTI dataset)

python tools/test.py --cfg_file tools/cfgs/kitti_models/DBQ-SSD.yaml  --batch_size ${BATCH_SIZE} --ckpt ${PTH_FILE}

# or

sh tools/scripts/dist_test.sh ${NUM_GPUS} \
    --cfg_file tools/cfgs/kitti_models/DBQ-SSD.yaml --batch_size ${BATCH_SIZE} --ckpt ${PTH_FILE}

Experimental results

KITTI dataset

Experiment results of different approaches on KITTI dataset (test set):

Quantitative results of different approaches on KITTI dataset (test set):

Experiment results of different approaches on Waymo dataset (validation set):

Experiment results of different approaches on ONCE dataset (validation set):

Visualization results of our DBQ-SSD on KITTI scene:

Visualization results of our DBQ-SSD on Waymo scene:

Citation

If you find this project useful in your research, please consider citing:

@inproceedings{dbqssd,
  title={Dbq-ssd: Dynamic Ball Query for Efficient 3D Object Detection},
  author={Yang, Jinrong and Song, Lin and Liu, Songtao and Mao, Weixin and Li, Zeming and Li, Xiaoping and Sun, Hongbin and Sun, Jian and Zheng, Nanning},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

Acknowledgement

  • This work is built upon the OpenPCDet (version 0.5) an open source toolbox for LiDAR-based 3D scene perception. Please refer to the official github repository for more information.
  • This work is also refer to the IA-SSD, the baseline model of our method.

License

This project is released under the Apache 2.0 license.