🔄 CycleVO

Part of the BodySLAM Framework for Endoscopic Surgical Applications

Paper | GitHub

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📌 Overview

CycleVO is an unsupervised monocular pose estimation model designed to robustly estimate the relative camera pose between consecutive frames from endoscopic video. It addresses challenges such as low-texture surfaces and significant illumination variations common in surgical environments.

✨ Key Features

• 🔄 Unsupervised Learning via Cycle Consistency: Inspired by CycleGAN and InfoGAN
• ⚡ Competitive Performance and Speed: Low inference time compared to state-of-the-art methods
• 🔌 Easy Integration with SLAM Pipelines: Provides ready-to-use motion matrices

🧠 Model Details

CycleVO learns to estimate the relative motion (i.e., camera pose) between consecutive endoscopic frames. The model predicts a motion matrix 𝑀=[𝑅,𝑡_unscaled,1,0] using a generator encoder architecture augmented with a pose estimation tail.

Developed by	Guido Manni, Clemente Lauretti, Francesco Prata, Rocco Papalia, Loredana Zollo, Paolo Soda
Model Type	Unsupervised Monocular Visual Odometry / Relative Camera Pose Estimation
License	MIT
Training	From scratch using a large-scale internal endoscopic dataset

🚀 Getting Started

For complete documentation, please refer to the GitHub repository.

🔍 Use Cases

✅ Ideal Applications

• Surgical Navigation: Real-time guidance during minimally invasive procedures
• 3D Reconstruction: Enhanced mapping of surgical scenes
• Depth Perception: Accurate pose estimates to complement monocular depth predictors

⛔ Out-of-Scope Applications

• General-purpose visual odometry without proper domain adaptation

📈 Training Details

• Dataset: 300+ hours of endoscopic videos from 100 patients (gastroscopy and prostatectomy)
• Preprocessing: Frame extraction with 128×128 pixel center crop
• Loss Function: Combined adversarial, image cycle consistency, and pose cycle consistency losses
• Optimizer: Adam with standard learning rate schedules

🛡️ Limitations & Recommendations

• Inherent Scale Ambiguity: Common in monocular systems
• Domain Specificity: Trained solely on endoscopic data
• Clinical Deployment: Requires thorough validation and clinical trials

We recommend:

• Validating the model thoroughly in your target environment
• Integrating additional sensors when possible
• Collaborating with clinical experts before surgical deployment

📚 Citation

@misc{manni2024bodyslamgeneralizedmonocularvisual,
      title={BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications}, 
      author={G. Manni and C. Lauretti and F. Prata and R. Papalia and L. Zollo and P. Soda},
      year={2024},
      eprint={2408.03078},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.03078}
}

📖 Glossary

• Cycle Consistency Loss: Enforces agreement between original and reconstructed inputs after transformations
• Motion Matrix (M): Composed of rotation (R) and unscaled translation vector (t_unscaled)
• ATE/RTE/RRE: Absolute Trajectory Error, Relative Trajectory Error, Relative Rotation Error

📫 Contact

For questions or further information, please contact: Guido Manni - [email protected]

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