CMD: A Cross Mechanism Domain Adaptation Dataset for 3D Object Detection(ECCV2024)

A dataset specifically designed for cross-mechanism domain adaptation, incorporating mechanical LiDARs with both high-res and low-res beams, solid-state LiDAR, and 4D millimeter-wave radar.

Download

This is the official GitHub repository for CMD(xmuda) dataset.

The download links can be found in (to update)
We also support Baidu Yunpan. Link： with extraction code: ****

Data Sample

Get Started

1. Installation and Data Preparation

a. Clone this repository.

git clone https://github.com/wayyeah/CMD-xmuda.git

b. Create virtual-env.

conda create -n xmuda python=3.8

b.1 cuda-11.4、cuda-11.6、cuda-11.7 tested

conda activate xmuda
pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116
pip install spconv-cu116	
pip install -r requirements.txt
python setup.py develop

b.2 cuda-12.x not tested

conda activate xmuda
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
pip install spconv-cu120
pip install -r requirements.txt
python setup.py develop

c.Download the dataset and run

cat xmuda.tar.gz.part* > xmuda.tar.gz
tar -xzvf xmuda.tar.gz xmuda
ln -s /path/to/xmuda /xmuda/data/xmu

All the file will be organized as,

xmuda
├── data
│   ├── xmu
│   │   │── ImageSets
│   │   │── calib_to_ego
│   │   │── ImageSets
|   |   |── label_ego
|   |   |── seq**     
├── pcdet
├── tools

Generate the data infos by running the following command:

 python -m pcdet.datasets.xmu.xmu_dataset --func create_xmu_infos  --cfg_file tools/cfgs/dataset_configs/xmu/xmuda_dataset.yaml

Generate gt_sampling_database by running the following command:

python -m pcdet.datasets.xmu.xmu_dataset --func create_groundtruth_database  --cfg_file tools/cfgs/dataset_configs/xmu/xmu_dataset.yaml

Due to our updated data annotation, the AP is different from what is presented in the paper

Model Zoo

3D Object Detection Baselines

Selected supported methods are shown in the below table. The results are the 3D detection performance on the val set of CMD dataset.

All LiDAR-based models are trained with 4 GTX 3090 GPUs and are available for download.

Ouster

AP@50	Car	Truck	Pedestrian	Cyclist	mAP
PointPillar	41.70	18.13	3.80	37.77	25.35
CenterPoint	40.43	18.77	11.47	45.76	29.11
Voxel-RCNN	43.20	21.70	13.70	41.32	29.98
VoxelNeXt	41.40	20.98	10.25	46.14	29.70

Robosense

AP@50	Car	Truck	Pedestrian	Cyclist	mAP
PointPillar	47.63	18.83	6.82	36.98	27.56
CenterPoint	49.16	21.21	2.79	44.82	29.50
Voxel-RCNN	50.61	23.97	12.86	43.17	32.65
VoxelNeXt	49.56	21.66	5.64	44.45	30.33

Hesai

AP@50	Car	Truck	Pedestrian	Cyclist	mAP
PointPillar	42.11	18.85	6.89	33.27	25.28
CenterPoint	42.39	19.15	4.02	37.88	25.86
Voxel-RCNN	44.85	21.84	11.63	34.81	28.28
VoxelNeXt	44.19	21.57	3.66	39.47	27.22

Training

cd ../../tools

*if you use singe gpu run

python train.py --cfg_file  cfgs/xmu_ouster_models/centerpoint.yaml

*if you use multi 8 gpus run

bash scripts/dist_train.sh 8 --cfg_file cfgs/xmu_ouster_models/centerpoint.yaml

Evaluation

*if you use singe gpu run

python test.py --cfg_file cfgs/xmu_ouster_models/centerpoint.yaml --ckpt /path/to/your/checkpoint

*if you use multi 8 gpus run

bash scripts/dist_test.sh 8 --cfg_file cfgs/xmu_ouster_models/centerpoint.yaml --ckpt /path/to/your/checkpoint

Todo List

Data of Radar.
Code of Radar and Camera.
Code of DIG.
Code of BEVFUSION.

Citation

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