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SSSSphinx/
SCoDe

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

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 - SCoDe model configuration
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 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 - Dataset and benchmarks
OETR - Model initialization strategies
PyTorch team for the excellent framework

Contact

For questions or issues, please visit the GitHub repository or contact the authors.

Paper: Scale-aware Co-visible Region Detection for Image Matching
Project Page: https://xupan.top/Projects/scode