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README.md

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+# 🔄 CycleVO
+
+<div align="center">
+
+![license: mit](https://img.shields.io/badge/license-MIT-blue)
+![tags: SLAM](https://img.shields.io/badge/tags-SLAM-brightgreen)
+![tags: Visual Odometry](https://img.shields.io/badge/tags-Visual%20Odometry-brightgreen)
+![tags: Computer Vision](https://img.shields.io/badge/tags-Computer%20Vision-brightgreen)
+![tags: Relative Camera Pose Estimation](https://img.shields.io/badge/tags-Relative%20Camera%20Pose%20Estimation-brightgreen)
+
+**Part of the BodySLAM Framework for Endoscopic Surgical Applications**
+
+[Paper](https://arxiv.org/abs/2408.03078) | [GitHub](https://github.com/GuidoManni/BodySLAM)
+
+</div>
+
 ---
-license: mit
-tags:
-- SLAM
-- Visual Odometry
-- Computer Vision
-- Relative Camera Pose Estimation
+
+## 📌 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.
+
+<div align="center">
+  <img src="https://via.placeholder.com/800x400?text=CycleVO+Architecture" alt="CycleVO Architecture Diagram" width="80%"/>
+</div>
+
+## ✨ 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<sub>unscaled</sub>,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](https://github.com/yourusername/BodySLAM).
+
+
+## 🔍 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
+
+```bibtex
+@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<sub>unscaled</sub>)
+- **ATE/RTE/RRE**: Absolute Trajectory Error, Relative Trajectory Error, Relative Rotation Error
+
+## 📫 Contact
+
+For questions or further information, please contact: 
+**Guido Manni** - [guido.manni@unicampus.it](mailto:guido.manni@unicampus.it)
+
 ---