Awesome
Structured Outdoor Architecture Reconstruction by Exploration and Classification
Fuyang Zhang, Xiang Xu, Nelson Nauata, Yasutaka Furukawa.
[arXiv
]
[Project Page
]
[Bibtex
]
In ICCV 2021
<img src="images/teaser.png" width="2000">
Prerequisites
- Linux
- NVIDIA GPU, CUDA 11+
- Python 3.7+, PyTorch 1.7+
Dependencies
Install additional dependencies:
$ pip install -r requirements.txt
Data
Download the processed data from this link. This includes the original cities dataset from "Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Classification" and predictions from Conv-MPN, IP and Per-Edge models.
Download the pretrained heatmap weights from this link.
Both data are required for training and evaluation, unzip and move them to the data
folder.
Running the Code
Training
python train_evaluators.py
This will start both the train and search threads.
You can change settings like beam search depth or number of training epochs in the config.py
.
Evaluation
First, perform beam search over all the test data:
python search_result.py
Then, evaluate the scores for all searched results:
python metric_for_result.py
Pretrained models
Download individual pretrained model and its beam search results.
Training Dataset | Model Weights | Beam Search Results |
---|---|---|
Conv-MPN | convmpn_weights.zip | convmpn_beamsearch.zip |
IP | ip_weights.zip | ip_beamsearch.zip |
Per-Edge | peredge_weights.zip | peredge_beamsearch.zip |
<a name="Citing"></a>Citation
If you find this code helpful, please consider citing:
@InProceedings{zhang2021structured,
title={Structured Outdoor Architecture Reconstruction by Exploration and Classification},
author={Fuyang Zhang and Xiang Xu and Nelson Nauata and Yasutaka Furukawa},
year={2021},
eprint={2108.07990},
archivePrefix={International Conference on Computer Vision (ICCV)},
primaryClass={cs.CV}
}
Contact
If you have any questions, please contact fuyangz@sfu.ca or xuxiangx@sfu.ca
Acknowledgement
This research is partially supported by NSERC Discovery Grants with Accelerator Supplements and DND/NSERC Discovery Grant Supplement.