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V2PNet

Voxel to Point Propagation and Fusion to Improve Feature Representation for Point Cloud Registration

Network Structure

fig1

Requirements

Thus requirements need to be met:

  • Ubuntu 14.04 or higher (Manjaro Linux 22.0.0 or higher)
  • CUDA 11.1 or higher
  • Python v3.7 or higher
  • Pytorch v1.6 or higher
  • MinkowskiEngine v0.5 or higher

Specifically, The code has been tested with: Manjaro Linux 22.0.0, CUDA 11.3.1, python 3.8.12, Pytorch 1.10.12, GeForce RTX 3080Ti.

Installation

First, create the conda environment:

conda env create -f v2pnet.yml -n v2pnet
conda activate v2pnet

Second, install MinkowskiEngine for voxelization and devoxelization, here we offer two ways according to MinkowskiEngine by using the version we offered:

cd MinkowskiEngine

# Install package openblas-devel
conda install openblas-devel -c anaconda

# Specify the file path according to the location of your cuda
export CUDA_HOME=/opt/cuda

# Install MinkowskiEngine
python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas=openblas
cd ..

# Or following official command installation by pypi:
pip install git+https://github.com/NVIDIA/MinkowskiEngine.git

Thrid, install cpp_wrappers

cd cpp_wrappers
sh compile_wrappers.sh
cd ..

Dataset

  • The datasets have been uploaded to Baidu Cloud.
  • 3DMatch, Code: 9ant.
  • KITTI, Code: rd5p.
├── dataset
│   ├──	3dmatch  
        ├── 7-scenes-redkitchen
        ├── sun3d-hotel_uc-scan3
        ├── sun3d-hotel_umd-maryland_hotel1
        ├── sun3d-hotel_umd-maryland_hotel3
        ├── sun3d-home_at-home_at_scan1_2013_jan_1
        ├── sun3d-mit_76_studyroom-76-1studyroom2
        ├── sun3d-home_md-home_md_scan9_2012_sep_30
        └── sun3d-mit_lab_hj-lab_hj_tea_nov_2_2012_scan1_erika
│   ├── kitti
        ├── dataset
            ├── poses
            └── sequences     
        ├── icp
        └── scene_list

Pretrained model

We provide the pre-trained model of 3DMatch pretrained model and KITTI pretrained model in ptrtrain_model.

Test

To test V2PNet, by command:

# Indoor-scene dataset 3DMatch
python test_3dmatch.py

The Registration Recall can be calculated by running the evaluate.m in geometric_registration/3dmatch (if you install matlab engine, you can run eva.py in geometric_registration/3dmatch/) which are provided by 3DMatch. You need to modify the descriptorName to V2PNet_{timestr} in the geometric_registration/3dmatch/evaluate.m file. You can change the number of keypoints in evaluate.py.

# Outdoor-scene dataset KITTI
python test_kitti.py

Discussion

Whether bias is added during the linear process in the training will have an effect on the results for reasons to be analyzed.

Acknowledgment

We would like to thank the open-source code of D3Feat, FCGF.