CADet: Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds

CADet is an one-stage 3D object detector proposed to handle the density variance in point cloud. We integrate our method into the awesome codebase PCDet. For more details of our work, please refer our paper https://arxiv.org/abs/1912.04775v3

AP on KITTI Dataset

Car [email protected],0.70,0.70:
bbox AP:90.82,89.71,88.15
bev  AP:90.28,87.11,83.92
3d   AP:88.51,78.20,75.74
aos  AP:90.80,89.48,87.80

Car [email protected],0.70,0.70:
bbox AP:95.52,92.13,90.66
bev  AP:92.55,88.22,86.33
3d   AP:88.84,79.43,75.95
aos  AP:95.49,91.86,90.27

Installation

The installation is following the steps in pcdet.

Requirements

All the codes are tested in the following environment:

• Linux (tested on Ubuntu 14.04/16.04)
• Python 3.6+
• PyTorch 1.1 or higher
• CUDA 9.0 or higher

Install cadet

1. Clone this repository.

git clone https://github.com/Hub-Tian/CADNet.git

2. Install the dependent libraries as follows:

• Install the dependent python libraries:

pip install -r requirements.txt 

• Install the SparseConv library, we extended the implementation from spconv.

cd spconv
python setup.py bdist_wheel
cd ../dist
pip install ./spconv*

3. Install this pcdet library by running the following command:

python setup.py develop

Dataset Preparation

Currently we only support KITTI dataset, and contributions are welcomed to support more datasets.

KITTI Dataset

• Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes could be downloaded from here, which are optional for data augmentation in the training):

PCDet
├── data
│   ├── kitti
│   │   │──ImageSets
│   │   │──training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │──testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools

• Generate the data infos by running the following command in the path pcdet/datasets/kitti:

python kitti_dataset.py create_kitti_infos

Getting Started

All the config files are within tools/cfgs/.

Test and evaluate the pretrained models

• Test with a pretrained model:

python test.py --cfg_file /tools/cfgs/pointpillar_expand_car.yaml --batch_size 4 --ckpt ${CKPT}

• To evaluate all the saved checkpoints of a specific training setting and draw the performance curve on the Tensorboard, add the --eval_all argument:

python test.py --cfg_file /tools/cfgs/pointpillar_expand_car.yaml --batch_size 4 --eval_all

Train a model

• Train with multiple GPUs:

bash scripts/dist_train.sh ${NUM_GPUS} \ 
    --cfg_file /tools/cfgs/pointpillar_expand_car.yaml --batch_size ${BATCH_SIZE}

• Train with multiple machines:

bash scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} ${NUM_GPUS} \ 
    --cfg_file /tools/cfgs/pointpillar_expand_car.yaml --batch_size ${BATCH_SIZE}

• Train with a single GPU:

python train.py --cfg_file /tools/cfgs/pointpillar_expand_car.yaml --batch_size ${BATCH_SIZE}

Acknowledgement

This repo is based on pcdet(https://github.com/open-mmlab/OpenPCDet).

Citation

If you find this work useful in your research, please consider cite:

@article{tian2019context,
  title={Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds},
  author={Tian, Yonglin and Huang, Lichao and Yu, Hui and Wu, Xiangbin and Li, Xuesong and Wang, Kunfeng and Wang, Zilei and Wang, Fei-Yue},
  journal={arXiv preprint  arXiv:1912.04775v3},
  year={2020}
}