https://github.com/svip-lab/planarreconstruction

[CVPR'19] Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
https://github.com/svip-lab/planarreconstruction

computer-vision cvpr2019 deep-learning pytorch

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[CVPR'19] Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding

• Host: GitHub
• URL: https://github.com/svip-lab/planarreconstruction
• Owner: svip-lab
• License: mit
• Created: 2019-02-25T10:50:31.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-02-10T09:52:46.000Z (9 months ago)
• Last Synced: 2024-04-30T20:33:59.420Z (7 months ago)
• Topics: computer-vision, cvpr2019, deep-learning, pytorch
• Language: Python
• Homepage:
• Size: 817 KB
• Stars: 355
• Watchers: 22
• Forks: 83
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PlanarReconstruction

PyTorch implementation of our CVPR 2019 paper:

[Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding](https://arxiv.org/pdf/1902.09777.pdf)

Zehao Yu\*,

[Jia Zheng](https://bertjiazheng.github.io/)\*,

[Dongze Lian](https://svip-lab.github.io/team/liandz.html),

[Zihan Zhou](https://faculty.ist.psu.edu/zzhou/Home.html),

[Shenghua Gao](http://sist.shanghaitech.edu.cn/sist_en/2018/0820/c3846a31775/page.htm)

(\* Equal Contribution)



## Getting Started

### Installation

Clone repository and use [git-lfs](https://git-lfs.github.com/) to fetch the trained model (or download [here](https://drive.google.com/file/d/1Aa1Jb0CGpiYXKHeTwpXAwcwu_yEqdkte/view?usp=sharing)):

```bash

git clone [email protected]:svip-lab/PlanarReconstruction.git

```

We use Python 3. Create an Anaconda enviroment and install the dependencies:

```bash

conda create -y -n plane python=3.6

conda activate plane

conda install -c menpo opencv

pip install -r requirements.txt

```

### Downloading and converting data

Please download the *.tfrecords* files for training and testing converted by [PlaneNet](https://github.com/art-programmer/PlaneNet), then convert the *.tfrecords* to *.npz* files:

```bash

python data_tools/convert_tfrecords.py --data_type=train --input_tfrecords_file=/path/to/planes_scannet_train.tfrecords --output_dir=/path/to/save/processd/data

python data_tools/convert_tfrecords.py --data_type=val --input_tfrecords_file=/path/to/planes_scannet_val.tfrecords --output_dir=/path/to/save/processd/data

```

### Training

Run the following command to train our network:

```bash

python main.py train with dataset.root_dir=/path/to/save/processd/data

```

### Evaluation

Run the following command to evaluate the performance:

```bash

python main.py eval with dataset.root_dir=/path/to/save/processd/data resume_dir=/path/to/pretrained.pt dataset.batch_size=1

```

### Prediction

Run the following command to predict on a single image:

```bash

python predict.py with resume_dir=pretrained.pt image_path=/path/to/image

```

## Acknowledgements

We thank [Chen Liu](http://art-programmer.github.io/index.html) for his great works and repos.

## Citation

Please cite our paper for any purpose of usage.

```

@inproceedings{YuZLZG19,

  author    = {Zehao Yu and Jia Zheng and Dongze Lian and Zihan Zhou and Shenghua Gao},

  title     = {Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding},

  booktitle = {CVPR},

  pages     = {1029--1037},

  year      = {2019}

}

```