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mfgnet's Introduction

Code for Paper "Multi-Features Guidance Network for partial-to-partial point cloud registration" (MFGNet)

This is the official code repository for "Multi-Features Guidance Network for partial-to-partial point cloud registration" [arxiv]. The code is largely adapted from https://github.com/jiahaowork/idam.

MFGNet architecture

Environment

python 3.7

torch 1.5.1 + cu101

Open3D 0.10.0.1

spicy 1.4.1

hyp5 2.10.0

tqdm 4.47.0

Usage

The easiest way to run the code is using the following command

python main.py --exp_name exp

This command will run an experiment on the ModelNet40 dataset (automatically downloaded) with all the options set to default. You can see at the end of main.py a list of options that can be used to control hyperparameters of the model and experiment settings. The comments in the file should be enough to understand them.

Citation

If you want to use it in your work, please cite it as

@article{wang2021multi,
	title={Multi-features guidance network for partial-to-partial point cloud registration},
	author={Wang, Hongyuan and Liu, Xiang and Kang, Wen and Yan, Zhiqiang and Wang, Bingwen and Ning, Qianhao},
	journal={Neural Computing and Applications},
	pages={1--12},
	year={2021},
	publisher={Springer}
	}

mfgnet's People

Contributors

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Watchers

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Forkers

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mfgnet's Issues

关于可视化

你好,我想问一下图5这种配准前和配准后的图是怎么画出来的呢??还有一个问题 我直接运行main.py时 会得到一个text的值 如果我用保存的模型去运行text.py的话 是不是也能得到相同的结果呢??

关于测试结果

你好,关于每次实验的测试结果,是在运行python main.py --exp_name exp后,在每次epoch得到的test指标中找最好的结果吗?

About Figure 3

May I ask which software was used to draw the renderings in Figure 3? If it is implemented by programming, can you tell me the idea of ​​programming? Thanks to the author for the answer

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