VRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filtering
Guiyu Zhao, Zewen Du, Zhentao Guo, Hongbin Ma.
This code has been tested with Python 3.9, Pytorch 1.11.0, CUDA 11.1 on Ubuntu 20.04.
- Clone the repository
git clone https://github.com/GuiyuZhao/VRHCF && cd VRHCF
- Setup conda virtual environment
conda create -n VRHCF python=3.9
source activate VRHCF
conda install pytorch==1.11.0 torchvision==0.12.0 cudatoolkit=11.3 -c pytorch
conda install -c open3d-admin open3d==0.11.1
pip install "git+git://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
- Prepare the datasets
You can download the 3DCSR dataset from 3DCSR benchmark, and download processed 3DMatch dataset from Baidu Yun (Verification code:6nkf).
Then, the data is organised as follows:
--data--3DMatch--fragments
|--intermediate-files-real
|--keypoints
|--patches
--data--3DCSR--kinect_lidar
|--kinect_sfm
The pretrained models and features can be downloaded from releases
Follow the SpinNet/SphereNet to download and place the 3DMatch dataset.
Train
Train SphereNet on the 3DMatch dataset:
cd ./ThreeDMatch/Train
python train.py
Test
Extract the descriptors by using our pre-trained model:
cd ./ThreeDMatch/Test
python preparation.py
The learned descriptors will be saved in ThreeDMatch/Test/SphereNet_{timestr}/ folder.
Evaluate
The extracted feature descriptors are used for feature matching and correspondence filtering to complete point cloud registration. Evaluate our method by running:
python eval_3DMatch.py [timestr] [samplings]
samplings is the number of keypoints. You can estimate the pose in any sampling numbers.
Test
Extract the descriptors by using our pre-trained model:
cd ./ThreeDMatch/Test
python preparation.py
The learned descriptors will be saved in ThreeDMatch/Test/SphereNet_{timestr}/ folder.
Evaluate
The extracted feature descriptors are used for feature matching and correspondence filtering to complete point cloud registration. Evaluate our method by running:
python eval_3DMatch.py [timestr] [samplings]
samplings is the number of keypoints. You can estimate the pose in any sampling numbers.
kinect-lidar
Evaluate on kinect-lidar by using our pre-trained model:
python eval_3DCSR_lidar.py [samplings]
kinect-lidar
Evaluate on kinect-lidar by using our pre-trained model:
python eval_3DCSR_sfm.py [samplings]
| Method | RR(%) | RE(°) | TE(cm) | FMR(%) | IR(%) |
|---|---|---|---|---|---|
| GeoTransformer | 92.00 | 1.62 | 5.30 | 97.90 | 71.90 |
| VRHCF | 96.21 | 1.81 | 6.02 | 97.35 | 87.76 |
| Benchmark | RR(%) | RE(°) | TE(cm) | FMR(%) | IR(%) |
|---|---|---|---|---|---|
| Kinect-sfm | 93.8 | 90.3 | 96.8 | 2.06 | 0.06 |
| Kinect-lidar | 10.3 | 6.4 | 13.6 | 3.41 | 0.13 |
In this project, we use parts of the implementations of the following works:
- 01/18/2024: The code is released!