PCReg.PyTorch Save
A Simple Point Cloud Registration Network based on PointNet.
Project README
Our recent registraion works:
- NgeNet: paper, code. We achieved SoTA RR (Registration Recall) in 3DMatch with 92.9%.
- ROPNet paper, code. Our solution based on ROPNet and OverlapPredator won the second place on the MVP Registration Challenge (ICCV Workshop 2021). [Technical Report]
Introduction
A Simple Point Cloud Registration Pipeline based on Deep Learning. Detailed Information Please Visit this Zhihu Blog.
Install
- requirements.txt
pip install -r requirements.txt - open3d-python==0.9.0.0
python -m pip install open3d==0.9 - emd loss
cd loss/cuda/emd_torch & python setup.py install
Start
-
Download data from [here,
435M] -
evaluate and show(download the pretrained checkpoint [Complete, pwd:
c4z7,16.09 M] or [Paritial, pwd:pcno,16.09] first)# Iterative Benchmark python modelnet40_evaluate.py --root your_data_path/modelnet40_ply_hdf5_2048 --checkpoint your_ckpt_path/test_min_loss.pth --cuda # Visualization # python modelnet40_evaluate.py --root your_data_path/modelnet40_ply_hdf5_2048 --checkpoint your_ckpt_path/test_min_loss.pth --show # ICP # python modelnet40_evaluate.py --root your_data_path/modelnet40_ply_hdf5_2048 --method icp # FGR # python modelnet40_evaluate.py --root your_data_path/modelnet40_ply_hdf5_2048 --method fgr --normal -
train
CUDA_VISIBLE_DEVICES=0 python modelnet40_train.py --root your_data_path/modelnet40_ply_hdf5_2048
Experiments
- Point-to-Point Correspondences(R error is large due to EMDLoss, see here)
| Method | isotropic R | isotropic t | anisotropic R(mse, mae) | anisotropic t(mse, mae) | time(s) |
|---|---|---|---|---|---|
| ICP | 11.44 | 0.16 | 17.64(5.48) | 0.22(0.07) | 0.07 |
| FGR | 0.01 | 0.00 | 0.07(0.00) | 0.00(0.00) | 0.19 |
| IBenchmark | 5.68 | 0.07 | 9.77(2.69) | 0.12(0.03) | 0.022 |
| IBenchmark + ICP | 3.65 | 0.04 | 9.22(1.66) | 0.11(0.02) |
- Noise Data(infer_npts = 1024)
| Method | isotropic R | isotropic t | anisotropic R(mse, mae) | anisotropic t(mse, mae) |
|---|---|---|---|---|
| ICP | 12.14 | 0.17 | 18.32(5.86) | 0.23(0.08) |
| FGR | 4.27 | 0.06 | 11.55(2.43) | 0.09(0.03) |
| IBenchmark | 6.25 | 0.08 | 9.28(2.94) | 0.12(0.04) |
| IBenchmark + ICP | 5.10 | 0.07 | 10.51(2.39) | 0.13(0.03) |
- Partial-to-Complete Registration(infer_npts = 1024)
| Method | isotropic R | isotropic t | anisotropic R(mse, mae) | anisotropic t(mse, mae) |
|---|---|---|---|---|
| ICP | 21.33 | 0.32 | 22.83(10.51) | 0.31(0.15) |
| FGR | 9.49 | 0.12 | 19.51(5.58) | 0.17(0.06) |
| IBenchmark | 15.02 | 0.22 | 15.78(7.45) | 0.21(0.10) |
| IBenchmark + ICP | 9.21 | 0.13 | 14.73(4.43) | 0.18(0.06) |
Note:
- Detailed metrics information please refer to RPM-Net[CVPR 2020].
Train your Own Data
- Prepare the data in the following structure
|- CustomData(dir) |- train_data(dir) - train1.pcd - train2.pcd - ... |- val_data(dir) - val1.pcd - val2.pcd - ... - Train
python custom_train.py --root your_datapath/CustomData --train_npts 2048 # Note: train_npts depends on your dataset - Evaluate
# Evaluate, infer_npts depends on your dataset python custom_evaluate.py --root your_datapath/CustomData --infer_npts 2048 --checkpoint work_dirs/models/checkpoints/test_min_loss.pth --cuda # Visualize, infer_npts depends on your dataset python custom_evaluate.py --root your_datapath/CustomData --infer_npts 2048 --checkpoint work_dirs/models/checkpoints/test_min_loss.pth --show
Acknowledgements
Thanks for the open source code for helping me to train the Point Cloud Registration Network successfully.
Open Source Agenda is not affiliated with "PCReg.PyTorch" Project. README Source: zhulf0804/PCReg.PyTorch
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