https://github.com/ShiPC-AI/TCF
A Two-stage Consensus Filtering for Real-time 3D Registration
https://github.com/ShiPC-AI/TCF
Last synced: about 1 year ago
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A Two-stage Consensus Filtering for Real-time 3D Registration
- Host: GitHub
- URL: https://github.com/ShiPC-AI/TCF
- Owner: ShiPC-AI
- License: mit
- Created: 2024-09-05T03:59:15.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-08T01:21:05.000Z (almost 2 years ago)
- Last Synced: 2024-09-08T02:57:01.011Z (almost 2 years ago)
- Language: C++
- Size: 5.37 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
TCF (Two-stage Consensus Filtering)
TCF is a 3D correspondence-based point cloud registration method. It elevates RANSAC to SOTA speed and accuracy. Firstly, one-point RANSAC obtains a consensus set based on length consistency. Subsequently, two-point RANSAC refines the set via angle consistency. Then, three-point RANSAC computes a coarse pose and removes outliers based on transformed correspondence’s distances. Drawing on optimizations from one-point and two-point RANSAC, three-point RANSAC requires only a few iterations. Eventually, an iterative reweighted least squares (IRLS) is applied to yield the optimal pose.
(a) Overall framework
(b) Outlier Removal
## News
- **2025.01:** The preprocessed dataset in TCF is available: [Dataset Available](https://drive.google.com/drive/folders/1uKN2pqymFc85tY13Cw7kUNop7uQOEOSB).
- **2024.11:** TCF has been accepted for *IEEE Robotics and Automation Letters*: [Published Paper](https://ieeexplore.ieee.org/document/10758239).
## Citation
If you use code or data of TCF in your academic research, please cite our paper:
```
@ARTICLE{10758239,
author={Shi, Pengcheng and Yan, Shaocheng and Xiao, Yilin and Liu, Xinyi and Zhang, Yongjun and Li, Jiayuan},
journal={IEEE Robotics and Automation Letters},
title={RANSAC Back to SOTA: A Two-Stage Consensus Filtering for Real-Time 3D Registration},
year={2024},
volume={9},
number={12},
pages={11881-11888},
doi={10.1109/LRA.2024.3502056}}
```
## Dependencies
- **CMake**
- **PCL** (Point Cloud Library)
- **Eigen ≥ 3.4** (for slicing matrix)
- **nlohmann_json** (for reading config files)
## File and data structure
### Configure files
```bash
config
│ ├── config_eth.json
│ └── config_kitti.json
```
### Data structure
```bash
data
├── ETH_TLS
│ └── facade
│ ├── s1_v0.1_sor.pcd
│ ├── s2_v0.1_sor.pcd
│ ├── s2_s1_top3.match
│ ├── s2-s1.pose
│ └── ...
├── KITTI
│ └── 09
│ ├── 210_v0.3.pcd
│ ├── 191_v0.3.pcd
│ ├── 210_191_top3.match
│ ├── 210-191.pose
│ └── ...
```
- **xx.json**: Contains paths for source point cloud, target point cloud, initial correspondences, and ground truth pose.
- **xx.pcd**: Source and target point cloud.
- **A_B_top3.match**: A correspondence file where A is source (left 3 columns) and B is target (right 3 columns).
- **xx.pose**: The 4*4 groundtruth pose from A to B
**Note:** Source and target point clouds are used to calculate resolution and estimate noise level, enabling **a genralized method without manual parameter adjustments**. If not available, values can be set manually, but correspondences remain necessary.
## How to use?
### Complie and run Demo
```bash
mkdir build && cd build
cmake ..
make -j8
./demo
```
### Run other data
To test with different data, simply update the data path in the config file.