3D Reconstruction and Plane Detection using Epipolar Geometry

Welcome to the 3D Reconstruction and Plane Detection project! This repository contains a Python script that performs 3D reconstruction from stereo images using epipolar geometry. It detects keypoints, matches them between images, computes the Fundamental and Essential matrices, reconstructs a 3D point cloud, and fits planes to the reconstructed points using RANSAC.

Introduction

This project demonstrates how to perform:

• 🔑 Keypoint detection using SIFT
• 🔗 Feature matching between two images
• 📐 Computation of the Fundamental and Essential matrices
• 🧭 Recovery of camera pose (rotation and translation)
• 🌐 Triangulation of matched points to create a 3D point cloud
• ✈️ Plane fitting using RANSAC
• 🎨 Visualization of keypoints, matches, epipolar lines, and planes

Features

• Keypoint Detection: Detects and computes descriptors using SIFT.
• Feature Matching: Matches features using BFMatcher with a ratio test.
• Epipolar Geometry: Calculates the Fundamental and Essential matrices.
• Pose Recovery: Determines the relative camera positions.
• 3D Reconstruction: Triangulates points to form a 3D point cloud.
• Plane Detection: Uses RANSAC to detect planes in the point cloud.
• Visualization: Provides comprehensive plotting functions for analysis.

Requirements

• 🐍 Python 3.6 or higher
• 📦 NumPy
• 👁️ OpenCV (cv2)
• 📊 Matplotlib

Installation

1. Clone the Repository

   git clone https://github.com/danielbob32/ComputerVisonImageMatching
   cd ComputerVisonImageMatching

2. Install Dependencies

Install the required Python packages:

pip install numpy opencv-python matplotlib

Usage

1. Prepare Your Data

• 📁 Create a directory for your data, e.g., data/example_3/.
• 🖼️ Place your stereo images I1.png and I2.png in this directory.
• 📄 Provide the camera intrinsic matrix K.txt in the same directory. The matrix should be in text format with comma-separated values.

2. Configure Parameters In the main() function of the script, you can adjust the following parameters:

data_dir = 'data/example_3'  # Path to your data directory
amount = 70                  # Number of matches and epipolar lines to display
planes_amount = 2            # Number of planes to detect
Ransac_iterations = 1000     # RANSAC iterations for plane fitting
threshold = 0.5              # Distance threshold for RANSAC

3. Run the Script

Execute the script using the command line:

python main.py

4. View the Results

The script will display various plots:

• 📷 Keypoints detected in both images
• 🔍 Matched features between images
• 📏 Matches with connecting lines
• 📐 Epipolar lines corresponding to the matches
• 🛰️ Planes fitted to the 3D point cloud
• 🧭 Normals of the detected planes projected onto the images

Example Results

Keypoint Detection

Feature Matching

Epipolar Lines

Plane Detection

Normals Projection

Note: The results directory should contain the generated images from running the script.

Project Structure

- main.py: Main Python script containing all functions and the main() function.
- data/: Directory containing sample images and camera matrix.
- results/: Directory where output images and plots are saved.
- README.md: Project documentation.

Contributing

Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.

1. Fork the repository.
2. Create your feature branch: git checkout -b feature/YourFeature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin feature/YourFeature
5. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or suggestions, please contact Daniel Bobritski:

📧 Email: danielbob32@gmail.com 💼 LinkedIn: Daniel Bobritski 🎮 Discord: danielxp13#9709