😍 SGHR

Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting

CVPR 2023

Haiping Wang^*,1, Yuan Liu^*,2, Zhen Dong^†,1, Yulan Guo³, Yu-Shen Liu⁴, Wenping Wang⁵ Bisheng Yang^†,1

¹Wuhan University    ²The University of Hong Kong    ³Sun Yat-sen University
⁴Tsinghua University    ⁵Texas A&M University
^*The first two authors contribute equally.    ^†Corresponding authors.

In this paper, we present a new method for the multiview registration of point cloud. Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses. However, constructing a densely-connected graph is time-consuming and contains lots of outlier edges, which makes the subsequent IRLS struggle to find correct poses. To address the above problems, we first propose to use a neural network to estimate the overlap between scan pairs, which enables us to construct a sparse but reliable pose graph. Then, we design a novel history reweighting function in the IRLS scheme, which has strong robustness to outlier edges on the graph. In comparison with existing multiview registration methods, our method achieves $11$\% higher registration recall on the 3DMatch dataset and $\sim13$\% lower registration errors on the ScanNet dataset while reducing $\sim70$\% required pairwise registrations. Comprehensive ablation studies are conducted to demonstrate the effectiveness of our designs.

| Paper | Poster | Video |

🆕 News

2024-04-13: Online demo file, use SGHR in the EASIEST way! Just follow here.
2023-05-13: An introduction video of SGHR on YouTube.
2023-04-04: Release SGHR on Arxiv.
2023-04-01: The code of SGHR is released.
2023-02-28: SGHR is accepted by CVPR 2023! 🎉🎉

✨ Pipeline

💻 Requirements

Here we offer the YOHO backbone SGHR. Thus YOHO requirements need to be met:

Ubuntu 14.04 or higher
CUDA 11.1 or higher
Python v3.7 or higher
Pytorch v1.6 or higher

Specifically, The code has been tested with:

Ubuntu 16.04, CUDA 11.1, python 3.7.10, Pytorch 1.7.1, GeForce RTX 2080Ti.
Ubuntu 20.04, CUDA 11.1, python 3.7.16, Pytorch 1.10.0, GeForce RTX 4090.

🔧 Installation

First, create the conda environment:

conda create -n sghr python=3.7
conda activate sghr

Second, intall Pytorch. We have checked version 1.7.1 and other versions can be referred to Official Set.
```
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch
```
Third, install other packages, here we use 0.8.0.0 version Open3d for Ubuntu 16.04:
```
pip install -r requirements.txt
```

Optional. If you want to use SGHR on your own dataset or run the demo.py, you should install MinkowskiEngine for FCGF/YOHO:

conda install openblas-devel -c anaconda
git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas=openblas

💾 Dataset & Pretrained model

The datasets are accessible in BaiduDesk(Code:oouk) and Google Cloud:

Trainset:

3DMatch_train;

Testset:

Datasets above contain the point clouds (.ply), keypoints (.txt, 5000 per point cloud), and rotation-invariant yoho-desc(.npy, extracted on the keypoints) files. Please place the data to ./data following the example data structure as:

data/
├── 3dmatch/
    └── kitchen/
        ├── PointCloud/
            ├── cloud_bin_0.ply
            ├── gt.log
            └── gt.info
        ├── yoho_desc/
            └── 0.npy
        └── Keypoints/
            └── cloud_bin_0Keypoints.txt
├── 3dmatch_train/
├── scannet/
└── ETH/

🚅 Train

You can train SGHR with the 3dmatch_train dataset downloaded above, where we offer the 32-dim rotation-invariant yoho-desc we extracted on 3dmatch_train and you can also extract 32-dim invariant yoho-desc(row-pooling on yoho-desc) yourself and save the features to '''data/3dmatch_train/\<scene>/yoho_desc'''. Then, you can train SGHR with the following commond:

python Train.py

✏️ Use SGHR in the easiest way!

Use SGHR is quite simple, prepare your point cloud files and no other effort needed! Follow here.

✏️ Test

Try SGHR on the demo files by:

python demo.py --pcdir data/demo

To evalute SGHR on 3DMatch and 3DLoMatch, you can use the following commands:

# extract global features
python Test.py --dataset 3dmatch
# conduct multiview registration
python Test_cycle.py --dataset 3dmatch --rr
# visualize the registration results
python visual.py --dataset 3dmatch

To evalute SGHR on ScanNet, you can use the following commands:

python Test.py --dataset scannet
python Test_cycle.py --dataset scannet --ecdf

To evalute SGHR on ETH, you can use the following commands:

python Test.py --dataset ETH
python Test_cycle.py --dataset ETH --topk 6 --inlierd 0.2 --tau_2 0.5 --rr

💡 Citation

Please consider citing SGHR if this program benefits your project

@inproceedings{
wang2023robust,
title={Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting},
author={Haiping Wang and Yuan Liu and Zhen Dong and Yulan Guo and Yu-Shen Liu and Wenping Wang and Bisheng Yang},
booktitle={Conference on Computer Vision and Pattern Recognition},
year={2023}
}

🔗 Related Projects

Take a look at our previous works on feature extraction and pairwise registration!

WHU-USI3DV / SGHR

readme