This is the official repository of SpinNet, a conceptually simple neural architecture to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. For technical details, please refer to:
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
Sheng Ao*, Qingyong Hu*, Bo Yang, Andrew Markham, Yulan Guo.
(* indicates equal contribution)
[Paper] [Video] [Project page]
This code has been tested with Python 3.6, Pytorch 1.6.0, CUDA 10.2 on Ubuntu 18.04.
- Clone the repository
git clone https://github.com/QingyongHu/SpinNet && cd SpinNet
- Setup conda virtual environment
conda create -n spinnet python=3.6
source activate spinnet
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -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"
Download the processed dataset from Google Drive, Baidu Yun (Verification code:d1vn) and put the folder into data
.
Then the structure should be as follows:
--data--3DMatch--fragments
|--intermediate-files-real
|--patches
Training
Training SpinNet on the 3DMatch dataset:
cd ./ThreeDMatch/Train
python train.py
Testing
Evaluate the performance of the trained models on the 3DMatch dataset:
cd ./ThreeDMatch/Test
python preparation.py
The learned descriptors for each point will be saved in ThreeDMatch/Test/SpinNet_{timestr}/
folder.
Then the Feature Matching Recall(FMR)
and Inlier Ratio(IR)
can be calculated by running:
python evaluate.py [timestr]
The ground truth poses have been put in the ThreeDMatch/Test/gt_result
folder.
The Registration Recall
can be calculated by running the evaluate.m
in ThreeDMatch/Test/3dmatch
which are provided by 3DMatch.
Note that, you need to modify the descriptorName
to SpinNet_{timestr}
in the ThreeDMatch/Test/3dmatch/evaluate.m
file.
Download the processed dataset from Google Drive, Baidu Yun (Verification code:d1vn), and put the folder into data
.
Then the structure is as follows:
--data--KITTI--dataset
|--icp
|--patches
Training
Training SpinNet on the KITTI dataset:
cd ./KITTI/Train/
python train.py
Testing
Evaluate the performance of the trained models on the KITTI dataset:
cd ./KITTI/Test/
python test_kitti.py
The test set can be downloaded from here, and put the folder into data
, then the structure is as follows:
--data--ETH--gazebo_summer
|--gazebo_winter
|--wood_autmn
|--wood_summer
3DMatch to ETH
Generalization from 3DMatch dataset to ETH dataset:
cd ./generalization/ThreeDMatch-to-ETH
python preparation.py
The descriptors for each point will be generated and saved in the generalization/ThreeDMatch-to-ETH/SpinNet_{timestr}/
folder.
Then the Feature Matching Recall
and inlier ratio
can be caluclated by running
python evaluate.py [timestr]
3DMatch to KITTI
Generalization from 3DMatch dataset to KITTI dataset:
cd ./generalization/ThreeDMatch-to-KITTI
python test.py
KITTI to 3DMatch
Generalization from KITTI dataset to 3DMatch dataset:
cd ./generalization/KITTI-to-ThreeDMatch
python preparation.py
The descriptors for each point will be generated and saved in generalization/KITTI-to-3DMatch/SpinNet_{timestr}/
folder.
Then the Feature Matching Recall
and inlier ratio
can be caluclated by running
python evaluate.py [timestr]
In this project, we use (parts of) the implementations of the following works:
If you find our work useful in your research, please consider citing:
@inproceedings{ao2020SpinNet,
title={SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration},
author={Ao, Sheng and Hu, Qingyong and Yang, Bo and Markham, Andrew and Guo, Yulan},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2021}
}
[1] 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions, Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser, CVPR 2017.
- 03/04/2021: The code is released!
- 01/03/2021: This paper has been accepted by CVPR 2021!
- 25/11/2020: Initial release!
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