Paper "Neighborhood-aware Geometric Encoding Network for Point Cloud Registration" was renamed to "Leveraging Inlier Correspondences Proportion for Point Cloud Registration" (NgeNet -> GCNet).
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Recall on 3DMatch and 3DLoMatch (correspondences RMSE below 0.2)
Dataset npairs Scene Recall (%) Pair Recall (%) 3DMatch 1279 92.9 93.9 3DLoMatch 1726 71.9 74.5 -
Recall on 3DMatch and 3DLoMatch (under 0.3m && 15 degrees)
Dataset npairs Pair Recall (%) 3DMatch 1623 95.0 3DLoMatch 1781 75.1 -
Results on Odometry KITTI
Dataset RTE(cm) RRE(°) Recall (%) Odometry KITTI 6.1 0.26 99.8 -
Results on MVP-RG
Dataset RRE(°) RTE RMSE MVP-RG 7.99 0.048 0.093
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All experiments were run on a RTX 3090 GPU with an Intel 8255C CPU at 2.50GHz CPU. Dependencies can be found in
requirements.txt. -
Compile python bindings
# Compile cd NgeNet/cpp_wrappers sh compile_wrappers.sh
Download pretrained weights for 3DMatch, 3DLoMatch, Odometry KITTI and MVP-RG from GoogleDrive or BaiduDisk (pwd: vr9g).
We adopt the 3DMatch and 3DLoMatch provided from PREDATOR, and download it here [936.1MB]. Unzip it, then we should get the following directories structure:
| -- indoor
| -- train (#82, cats: #54)
| -- 7-scenes-chess
| -- 7-scenes-fire
| -- ...
| -- sun3d-mit_w20_athena-sc_athena_oct_29_2012_scan1_erika_4
| -- test (#8, cats: #8)
| -- 7-scenes-redkitchen
| -- sun3d-home_md-home_md_scan9_2012_sep_30
| -- ...
| -- sun3d-mit_lab_hj-lab_hj_tea_nov_2_2012_scan1_erika
## Reconfigure configs/threedmatch.yaml by updating the following values based on your dataset.
# exp_dir: your_saved_path for checkpoints and summary.
# root: your_data_path for the 3dMatch dataset.
cd NgeNet
python train.py configs/threedmatch.yaml
# note: The code `torch.cuda.empty_cache()` in `train.py` has some impact on the training speed.
# You can remove it or change its postion according to your GPU memory.
cd NgeNet
python eval_3dmatch.py --benchmark 3DMatch --data_root your_path/indoor --checkpoint your_path/3dmatch.pth --saved_path work_dirs/3dmatch [--vis] [--no_cuda]
python eval_3dmatch.py --benchmark 3DLoMatch --data_root your_path/indoor --checkpoint your_path/3dmatch.pth --saved_path work_dirs/3dlomatch [--vis] [--no_cuda]
Download odometry kitti here with [velodyne laser data, 80 GB] and [ground truth poses (4 MB)], then unzip and organize in the following format.
| -- kitti
| -- dataset
| -- poses (#11 txt)
| -- sequences (#11 / #22)
| -- icp (generated automatically when training and testing)
| -- 0_0_11.npy
| -- ...
| -- 9_992_1004.npy
## Reconfigure configs/kitti.yaml by updating the following values based on your dataset.
# exp_dir: your_saved_path for checkpoints and summary.
# root: your_data_path for the Odometry KITTI.
cd NgeNet
python train.py configs/kitti.yaml
cd NgeNet
python eval_kitti.py --data_root your_path/kitti --checkpoint your_path/kitti.pth [--vis] [--no_cuda]
Download MVP-RG dataset here, then organize in the following format.
| -- mvp_rg
| -- MVP_Train_RG.h5
| -- MVP_Test_RG.h5
## Reconfigure configs/mvp_rg.yaml by updating the following values based on your dataset.
# exp_dir: your_saved_path for checkpoints and summary.
# root: your_data_path for the MVP-RG.
python train.py configs/mvp_rg.yaml
# note: The code `torch.cuda.empty_cache()` in `train.py` has some impact on the training speed.
# You can remove it or change its postion according to your GPU memory.
python eval_mvp_rg.py --data_root your_path/mvp_rg --checkpoint your_path/mvp_rg.pth [--vis] [--no_cuda]
python demo.py --src_path demo_data/cloud_bin_21.pth --tgt_path demo_data/cloud_bin_34.pth --checkpoint your_path/3dmatch.pth --voxel_size 0.025 --npts 5000
python demo.py --src_path demo_data/src1.ply --tgt_path demo_data/tgt1.ply --checkpoint your_path/3dmatch.pth --voxel_size 0.025 --npts 20000
python demo.py --src_path demo_data/src2.ply --tgt_path demo_data/tgt2.ply --checkpoint your_path/3dmatch.pth --voxel_size 3 --npts 20000
Set an appropriate voxel_size for your test data. If you want to test on point cloud pair with large amount of points, please set a large voxel_size according to your data.
@article{zhu2022leveraging,
title={Leveraging Inlier Correspondences Proportion for Point Cloud Registration},
author={Zhu, Lifa and Guan, Haining and Lin, Changwei and Han, Renmin},
journal={arXiv preprint arXiv:2201.12094},
year={2022}
}
Thanks for the open source code OverlapPredator, KPConv-PyTorch, KPConv.pytorch, FCGF, D3Feat.pytorch, MVP_Benchmark and ROPNet.
