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PointNetLK Revisited

License: MIT

Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey

Will appear on 2021 Conference on Computer Vision and Pattern Recognition (CVPR) (oral)

arXiv link: https://arxiv.org/pdf/2008.09527.pdf

ModelNet40 3DMatch KITTI

Prerequisites

This code is based on PyTorch implementation, and tested on 1.0.0<=torch<=1.6.0. You may go to the PyTorch official site (https://pytorch.org/) to decide which torch/torchvision version is suitable for your system. You may also need to go to the tensorflow website (https://www.tensorflow.org/install) to download tensorboard. Other packages can be installed through,

pip install -r requirements.txt

NOTE: we use open3d interactive visualization window in the jupyter notebook, which is only available for open3d>=0.13.0. You may need to wait for a few seconds for the interactive visualization to show.

Demo Notebook

We provide a jupyter notebook (you may need to install the jupyter notebook) of toy example in the demo folder. You can run ./demo/test_toysample.ipynb to play with a point cloud pairs from 3DMatch dataset.

Here is a short video showing how to run the demo.

Demo notebook

Dataset

You may download dataset used in the paper from these websites.

ModelNet40 ShapeNet KITTI
https://modelnet.cs.princeton.edu https://shapenet.org http://www.cvlibs.net/datasets/kitti/eval_odometry.php

We used 3DMatch dataset prepared by authors of Deep Global Registration, you may download using this script provided by them, https://github.com/chrischoy/DeepGlobalRegistration/blob/master/scripts/download_3dmatch.sh.

After you download the dataset, you can create a symbolic link in the ./dataset folder as ./dataset/ModelNet and ./dataset/ThreeDMatch.

Notation on voxelization

In the current setting, the voxelization for overlapped source and target point clouds still relies on "registered point clouds" (such as results from a global registration). The code for voxelization after "transformation" is updated by setting argument --voxel_after_transf to True. In the real-world application, our algorithm would be better suited for local registration problem.

Training

python train.py

Evaluation

python test.py

Pre-Trained Models

You can find the pre-trained model in logs/model_trained_on_ModelNet40_model_best.pth. This model is trained on ModelNet40 dataset, and is used to produce most results (except results of trained/tested on 3DMatch dataset) in the paper.

Acknowledgement

This code is mostly adapted from the original PointNetLK, https://github.com/hmgoforth/PointNetLK.

The 3DMatch data loader code was adapted from the Deep Global Registration, https://github.com/chrischoy/DeepGlobalRegistration.

Part of the data voxelization code was adapted from the SECOND, https://github.com/traveller59/second.pytorch.

Part of the metric computaion code was adapted from the Deep Closest Point, https://github.com/WangYueFt/dcp.

Contributing

If you find the project useful for your research, you may cite,

@InProceedings{Li_2021_CVPR,
    author    = {Li, Xueqian and Pontes, Jhony Kaesemodel and Lucey, Simon},
    title     = {PointNetLK Revisited},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {12763-12772}
}