jing222/syn_masonry_bridge

🏗️ BridgePoint-Seg Dataset

BridgePoint-Seg is a synthetic 3D point cloud dataset developed for large-scale masonry bridge segmentation. It provides training and test sets of point clouds with detailed semantic labels across straight and curved masonry bridges.

📁 Dataset Structure

BridgePoint-Seg/
├── syn_data/
│   ├── train/
│   │   ├── straight_bridge/      # 2,177 training samples
│   │   └── curved_bridge/        # 1,500 training samples
│   └── test/
│       ├── straight_bridge/      # 87 test samples
│       └── curved_bridge/        # 500 test samples

Each point cloud sample includes:

• points.npz: A NumPy file containing a point cloud of shape (N, 3) with key 'xyz'.
• points_label.npz: A NumPy file containing per-point semantic labels with key 'sem_label'.

🧾 File Format

File	Content	Key	Shape
points.npz	3D coordinates of point cloud	xyz	(N, 3)
points_label.npz	Semantic labels per point	sem_label	(N,)

📊 Statistics

Set	Category	Samples
train	straight_bridge	2,177
train	curved_bridge	1,500
test	straight_bridge	87
test	curved_bridge	500

🧠 Applications

BridgePoint-Seg supports research on:

• Semantic segmentation of large-scale point clouds
• Generalization to bridge structures with different geometries
• Training lightweight deep learning architectures for infrastructure monitoring

Citations

If you find the code is beneficial to your research, please consider citing:

@article{jing2024lightweight,
  title={A lightweight Transformer-based neural network for large-scale masonry arch bridge point cloud segmentation},
  author={Jing, Yixiong and Sheil, Brian and Acikgoz, Sinan},
  journal={Computer-Aided Civil and Infrastructure Engineering},
  year={2024},
  publisher={Wiley Online Library}
}

@article{jing2022segmentation,
  title={Segmentation of large-scale masonry arch bridge point clouds with a synthetic simulator and the BridgeNet neural network},
  author={Jing, Yixiong and Sheil, Brian and Acikgoz, Sinan},
  journal={Automation in Construction},
  volume={142},
  pages={104459},
  year={2022},
  publisher={Elsevier}
}

License

Our work is subjected to MIT License.