📃 DMS: Low-overlap Registration of 3D Point Clouds with Double-layer Multi-scale Star-graph

DMS: Low-overlap Registration of 3D Point Clouds with Double-layer Multi-scale Star-graph.

Hualong Cao, Yongcai Wang, and Deying Li.

Pipline

🛠️ Setup

The code has been tested on:

• Ubuntu 20.04 LTS, Python 3.8.0, CUDA 11.6, torch1.7.1+cu110, GeForce RTX 3090

💻 Installation

Please use the following command for installation.

• Clone the repository

git clone https://github.com/HualongCao/DMS && cd DMS

# It is recommended to create a new environment
conda create -n dms python==3.8
conda activate dms
pip install torch==1.7.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html

# Install packages and other dependencies
pip install -r requirements.txt
python setup.py build develop

📦 Data Preparation

3DMatch

The dataset can be downloaded from PREDATOR. The data should be organized as follows:

--data--3DMatch--metadata
              |--data--train--7-scenes-chess--cloud_bin_0.pth
                    |      |               |--...
                    |      |--...
                    |--test--7-scenes-redkitchen--cloud_bin_0.pth
                          |                    |--...
                          |--...

Kitti odometry

Download the data from the Kitti official website into data/Kitti and run data/Kitti/downsample_pcd.py to generate the data. The data should be organized as follows:

--data--Kitti--metadata
            |--sequences--00--velodyne--000000.bin
            |              |         |--...
            |              |...
            |--downsampled--00--000000.npy
                         |   |--...

ModelNet

Download the data and run data/ModelNet/split_data.py to generate the data. The data should be organized as follows:

--data--ModelNet--modelnet_ply_hdf5_2048--...
               |--train.pkl
               |--val.pkl
               |--test.pkl

4DMatch

Download the data. The data should be organized as follows:

--data--4DMatch
               |--train--abe_CoverToStand--cam1_0000_cam2_0006.npz
               |      |               |--...
               |      |--...
               |--test--amy_Situps--cam1_0000_cam2_0003.npz
                      |                    |--...
                      |--...

🚀 Running

Training

The code is in experiments/exp. Use the following command for training.

CUDA_VISIBLE_DEVICES=0 python trainval.py

🎡 Multi-GPU Training

As the point clouds usually have different sizes, we organize them in the pack mode. This causes difficulty for batch training as we need to convert the data between batch mode and pack mode frequently. For this reason, we limit the batch size to 1 per GPU at this time and support batch training via DistributedDataParallel. Use torch.distributed.launch for multi-gpu training:

CUDA_VISIBLE_DEVICES=GPUS python -m torch.distributed.launch --nproc_per_node=NGPUS trainval.py

Note that the learning rate is multiplied by the number of GPUs by default as the batch size increased. In our experiments, multi-gpu training slightly improves the performance.

🎓 Citation

@ARTICLE{DMS,
  author={Cao, Hualong and Wang, Yongcai and Li, Deying},
  journal={IEEE Transactions on Visualization and Computer Graphics}, 
  title={DMS: Low-overlap Registration of 3D Point Clouds with Double-layer Multi-scale Star-graph}, 
  year={2024},
  volume={},
  number={},
  pages={1-16},
  doi={10.1109/TVCG.2024.3400822}
}

✏️ Acknowledgements

• GeoTransformer
• Maximal-Cliques
• PREDATOR
• CoFiNet