Fuyang Zhang, Xiang Xu, Nelson Nauata, Yasutaka Furukawa.

[arXiv] [Project Page] [Bibtex]

In ICCV 2021

• Linux
• NVIDIA GPU, CUDA 11+
• Python 3.7+, PyTorch 1.7+

Install additional dependencies:

$ pip install -r requirements.txt

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.

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.

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

Download individual pretrained model and its beam search results.

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}
}

If you have any questions, please contact fuyangz@sfu.ca or xuxiangx@sfu.ca

This research is partially supported by NSERC Discovery Grants with Accelerator Supplements and DND/NSERC Discovery Grant Supplement.