---
language:
- en
license: apache-2.0
tags:
- computer-vision
- image-matching
- overlap-detection
- feature-extraction
datasets:
- SSSSphinx/SCoDe
---

# SCoDe: Scale-aware Co-visible Region Detection for Image Matching

<div align="center">

[![Paper](https://img.shields.io/badge/Paper-ScienceDirect-green)](https://www.sciencedirect.com/science/article/abs/pii/S0924271625003260)
[![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.isprsjprs.2025.08.015-orange)](https://doi.org/10.1016/j.isprsjprs.2025.08.015)
[![Project Page](https://img.shields.io/badge/Project-Website-blue)](https://xupan.top/Projects/scode)
[![GitHub](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/SSSSphinx/SCoDe)

</div>

## Overview

SCoDe is a scale-aware co-visible region detection model designed for robust image matching. It detects overlapping regions between image pairs while being invariant to scale variations, making it particularly effective for structure-from-motion and 3D reconstruction tasks.

This model is built upon the CCOE (Co-visible region detection with Overlap Estimation) architecture and has been trained on the MegaDepth dataset.

## Model Details

- **Architecture**: CCOE-based transformer with multi-scale attention
- **Backbone**: ResNet-50
- **Input Size**: 1024×1024 (configurable)
- **Training Dataset**: MegaDepth
- **Framework**: PyTorch

### Key Features

- Scale-aware overlap region detection
- Rotation-invariant matching capabilities
- End-to-end trainable pipeline
- Compatible with various feature extractors (SIFT, SuperPoint, D2-Net, R2D2, DISK)

## Usage

### Installation

```bash
pip install torch torchvision
git clone https://github.com/SSSSphinx/SCoDe.git
cd SCoDe
pip install -r requirements.txt
```

### Quick Start

```python
import torch
from src.config.default import get_cfg_defaults
from src.model import CCOE

# Load configuration
cfg = get_cfg_defaults()
cfg.merge_from_file('configs/scode_config.py')

# Initialize model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = CCOE(cfg.CCOE).eval().to(device)

# Load pre-trained weights
model.load_state_dict(torch.load('weights/scode.pth', map_location=device))

# Model is ready for inference
with torch.no_grad():
    # Process image pair (example)
    image1 = torch.randn(1, 3, 1024, 1024).to(device)
    image2 = torch.randn(1, 3, 1024, 1024).to(device)
    output = model({'image1': image1, 'image2': image2})
```

### Training

```bash
# Single GPU training
python train_scode.py --num_workers 4 --epoch 15 --batch_size 4 --validation --learning_rate 1e-5

# Multi-GPU distributed training (4 GPUs)
python -m torch.distributed.launch --nproc_per_node 4 --master_port=29501 train_scode.py \
    --num_workers 4 --epoch 15 --batch_size 4 --validation --learning_rate 1e-5
```

### Evaluation

#### Rotation Invariance Evaluation
```bash
python rot_inv_eval.py \
    --extractors superpoint d2net r2d2 disk \
    --image_pairs path/to/image/pairs \
    --output_dir outputs/scode_rot_eval
```

#### Pose Estimation Evaluation
```bash
python eval_pose_estimation.py \
    --results_dir outputs/megadepth_results \
    --dataset megadepth
```

#### Radar Evaluation
```bash
python eval_radar.py \
    --results_dir outputs/radar_results
```

## Configuration

Main configuration files:
- [`configs/scode_config.py`](configs/scode_config.py) - SCoDe model configuration
- [`src/config/default.py`](src/config/default.py) - Default configuration template

### Key Parameters

```python
# Training
cfg.DATASET.TRAIN.IMAGE_SIZE = [1024, 1024]
cfg.DATASET.TRAIN.BATCH_SIZE = 4
cfg.DATASET.TRAIN.PAIRS_LENGTH = 128000

# Validation
cfg.DATASET.VAL.IMAGE_SIZE = [1024, 1024]

# Model
cfg.CCOE.BACKBONE.NUM_LAYERS = 50
cfg.CCOE.BACKBONE.STRIDE = 32
cfg.CCOE.CCA.DEPTH = [2, 2, 2, 2]
cfg.CCOE.CCA.NUM_HEADS = [8, 8, 8, 8]
```

## Dataset

The model is trained on the [MegaDepth](https://github.com/zhengqili/MegaDepth) dataset with scale-aware pair generation.

Dataset preparation:
```bash
python dataset_preparation.py \
    --base_path dataset/megadepth/MegaDepth \
    --num_per_scene 5000
```

Validation pairs are automatically generated and evaluated during training.

## Model Performance

SCoDe demonstrates strong performance on:
- **Rotation Invariance**: Robust to image rotations up to 360°
- **Scale Invariance**: Effective across multiple image scales
- **Pose Estimation**: Improved camera pose estimation on MegaDepth benchmark
- **Feature Matching**: Enhanced matching accuracy with various feature extractors

## Supported Feature Extractors

The model works seamlessly with:
- SIFT (with brute-force matcher)
- SuperPoint (with NN matcher)
- D2-Net
- R2D2
- DISK

## Citation

If you find this project useful in your research, please cite our paper:

```bibtex
@article{pan2025scale,
  title={Scale-aware co-visible region detection for image matching},
  author={Pan, Xu and Xia, Zimin and Zheng, Xianwei},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={229},
  pages={122--137},
  year={2025},
  publisher={Elsevier}
}
```

## License

This project is licensed under the Apache-2.0 License. See the LICENSE file for details.

## Acknowledgments

- [MegaDepth](https://github.com/zhengqili/MegaDepth) - Dataset and benchmarks
- [OETR](https://github.com/TencentYoutuResearch/ImageMatching-OETR) - Model initialization strategies
- PyTorch team for the excellent framework

## Contact

For questions or issues, please visit the [GitHub repository](https://github.com/SSSSphinx/SCoDe) or contact the authors.

---

**Paper**: [Scale-aware Co-visible Region Detection for Image Matching](https://www.sciencedirect.com/science/article/abs/pii/S0924271625003260)  
**Project Page**: [https://xupan.top/Projects/scode](https://xupan.top/Projects/scode)