PointAugmenting: Cross-Modal Augmentation for 3D Object Detection

GT-Paste data augmentation for cross-modal 3D object detection, which preserves the consistency between camera and LiDAR data.

CVPR21 - PointAugmenting: Cross-Modal Augmentation for 3D Object Detection
Chunwei Wang, Chao Ma, Ming Zhu, Xiaokang Yang

@inproceedings{wang2021pointaugmenting,
  title={PointAugmenting: Cross-Modal Augmentation for 3D Object  Detection},
  author={Wang, Chunwei and Ma, Chao and Zhu, Ming and Yang, Xiaokang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11794--11803},
  year={2021}
}

Main results

3D detection on nuScenes test set

	MAP ↑	NDS ↑	Car	Truck	C.V.	Bus	Trailer	Barrier	Motor.	Bicycle	Ped.	T.C.
CenterPoint	60.3	67.3	85.2	53.5	20.0	63.6	56.0	71.1	59.5	30.7	84.6	78.4
PointAugmenting	66.8	71.0	87.5	57.3	28.0	65.2	60.7	72.6	74.3	50.9	87.9	83.6

Installation

Please refer to the installation and usage of CenterPoint.

Image Backbone

load DCNv2

cd det3d/models/img_backbones
git clone https://github.com/CharlesShang/DCNv2
cd DCNv2
sh make.sh

For 2D image feature extraction, we use the pretrained DLA34 model from CenterTrack. Please download the model and put it into file folder /pretrained_model.

Data Preparation

Modified from CenterPoint's original document.

Step 1: Download data and organise as follows

# For nuScenes Dataset         
└── NUSCENES_DATASET_ROOT
       ├── samples       <-- key frames
       ├── sweeps        <-- frames without annotation
       ├── maps          <-- unused
       ├── v1.0-trainval <-- metadata

Step 2: Create a symlink to the dataset root

mkdir data && cd data
ln -s DATA_ROOT 
mv DATA_ROOT nuScenes # rename to nuScenes

Step 3: Create data

# nuScenes
python tools/create_data.py nuscenes_data_prep --root_path=NUSCENES_TRAINVAL_DATASET_ROOT --version="v1.0-trainval" --nsweeps=10 --rate==1

In the end, the data and info files should be organized as follows

# For nuScenes Dataset 
└── PointAugmenting
       └── data    
              └── nuScenes 
                     ├── samples       <-- key frames
                     ├── sweeps        <-- frames without annotation
                     ├── maps          <-- unused
                     |── v1.0-trainval <-- metadata and annotations
                     |── infos_train_10sweeps_withvelo_filter_True_100rate_crossmodal.pkl <-- train annotations
                     |── infos_val_10sweeps_withvelo_filter_True_crossmodal.pkl <-- val annotations
                     |── dbinfos_100rate_10sweeps_withvelo_crossmodal.pkl <-- GT database info files
                     |── gt_database_100rate_10sweeps_withvelo_crossmodal <-- GT database 

Train & Evaluate

Modified from CenterPoint's original document.

Use the following command to start a distributed training using 4 GPUs. The models and logs will be saved to work_dirs/CONFIG_NAME

python -m torch.distributed.launch --nproc_per_node=4 ./tools/train.py --config=CONFIG_PATH

For distributed testing with 4 gpus,

python -m torch.distributed.launch --nproc_per_node=4 ./tools/dist_test.py --config=CONFIG_PATH --work_dir work_dirs/CONFIG_NAME --checkpoint work_dirs/CONFIG_NAME/latest.pth

Acknowlegement

This project is not possible without multiple great opensourced codebases. We list some notable examples below.

• CenterPoint
• det3d
• CenterTrack
• CenterNet