PlueckerNet: Learn to Register 3D Line Reconstructions

This contains the datasets and codes for training the 3D line registration method described in : PlueckerNet: Learn to Register 3D Line Reconstructions, CVPR2021.

Datasets

Please download Structured3D and Semantic3D and Apollo datasets.

Please put the downloaded files under the folder ./dataset

Codes and Models

Prerequisites

Pytorch=1.1.0 : (This is the version on my PC, but I think it also works on yours)

numpy

opencv

tensorboardX

easydict

logging

json

If you find missing Prerequisites, please Google and install them using conda or pip

Overview

Our model is implemented in Pytorch. All our models are trained from scratch, please run the training codes to obtain models.

For pre-trained models, please download. Under the folder of each dataset, there is a folder named preTrained and you can find it there.

Please put the downloaded pre-trained models under the folder ./output

Training

Run:

python main_train.py

Testing

Run:

python main_test.py

If you have questions, please first refer to comments in scripts.

Publications

If you like, you can cite the following publication:

Liu, Liu, Hongdong Li, Haodong Yao, and Ruyi Zha. "PlueckerNet: Learn to Register 3D Line Reconstructions." arXiv preprint arXiv:2012.01096 (2020).

@article{liu2020plueckernet,
  title={PlueckerNet: Learn to Register 3D Line Reconstructions},
  author={Liu, Liu and Li, Hongdong and Yao, Haodong and Zha, Ruyi},
  journal={arXiv preprint arXiv:2012.01096},
  year={2020}
}

Contact

If you have any questions (NOT those you can find answers via Google), drop me an email (Liu.Liu@anu.edu.au)