An OpenCV-based 360-degree surround view system that processes fisheye camera images to create a bird's eye view for vehicle parking assistance and navigation.

• Multi-Camera Fisheye Processing: Supports 4 fisheye cameras (front, back, left, right)
• Automatic Corner Detection: Uses advanced histogram-based algorithms for calibration board detection
• Real-time Undistortion: Efficient fisheye-to-rectilinear image transformation
• Perspective Transformation: Converts undistorted images to bird's eye view
• Seamless Stitching: Blends multiple camera views into a single panoramic image
• Vehicle Overlay: Adds vehicle model overlay for spatial reference

• OpenCV 3.0+ (4.x recommended)
• CMake 3.10+
• GCC/Clang with C++11 support
• Linux/macOS/Windows (WSL2 recommended for Windows)

# Clone the repository
git clone https://github.com/xixu-me/AVM.git
cd AVM

# Make scripts executable
chmod +x scripts/*.sh

# Build the project
./scripts/build.sh

For detailed installation instructions, see INSTALLATION.md.

# Execute the AVM system with provided sample images
./scripts/run.sh

After successful execution, you'll find the following files in the build/ directory:

• stitched_result_with_su7.jpg - Final 360° panoramic view
• bird_*.jpg - Individual bird's eye view images
• *_undis.jpg - Undistorted fisheye images

The AVM system processes four fisheye camera images positioned around the vehicle:

Front Camera	Back Camera
Front fisheye camera capturing forward area	Rear fisheye camera capturing backward area

Left Camera	Right Camera
Left side fisheye camera	Right side fisheye camera

• Resolution: 1280×960 pixels
• Format: PNG/JPG
• Camera Type: Fisheye lens with wide FOV
• Calibration: 2×4 rectangular grid pattern visible in each image
• Bit Depth: 8-bit RGB

Each input image must contain a 2×4 rectangular calibration pattern:

• Grid Size: 2 rows × 4 columns (8 corners total)
• Visibility: Pattern must be clearly visible in overlapping areas
• Contrast: High contrast between pattern and ground
• Position: Within the valid detection region (20%-70% of image height)

The AVM system generates multiple intermediate and final output images:

Stage	Description	Example
1. Undistorted Images	Fisheye correction applied
2. Bird's Eye View	Perspective transformation
3. Final Stitched Result	Complete 360° view

The final stitched image provides a complete 360-degree around view:

Complete 360° Around View Monitor output with vehicle overlay

• Purpose: Corrected fisheye distortion
• Resolution: 1280×960 pixels
• Features: Corner points marked, distortion removed

• Front/Back Views: 792×305 pixels
• Left/Right Views: 1131×281 pixels
• Perspective: Top-down view transformation
• Rotation: Automatically corrected for proper orientation

• Contrast Enhanced (*_img_contrast.jpg): Gamma-corrected for corner detection
• Thresholded (*_img_thresh.jpg): Binary images showing detected features
• Corner Detection (*_undis_1.jpg): Undistorted images with detected corners marked

• Composition: All four camera views seamlessly blended
• Vehicle Overlay: Su7 vehicle model positioned at center
• Dimensions: Variable based on camera layout
• Blending: Smooth transitions using mask images

[Fisheye Images] → [Undistortion] → [Corner Detection] → [Bird's Eye Transform] → [Image Stitching] → [Final Output]

1. Undistortion Engine: Converts fisheye to rectilinear projection
2. Corner Detector: Histogram-based calibration pattern detection
3. Perspective Transform: Homography-based bird's eye view generation
4. Image Stitcher: Mask-based seamless image blending

The system uses a polynomial fisheye distortion model:

θ_distorted = θ_undistorted + k₁θ³ + k₂θ⁵ + k₃θ⁷ + k₄θ⁹

Default parameters optimized for automotive fisheye cameras.

Key parameters that can be adjusted in src/avm.cpp:

// Camera intrinsics
float m_focal_length = 910.0;      // Camera focal length (pixels)
float m_fish_scale = 0.5;          // Fisheye scaling factor
float m_undis_scale = 1.55;        // Undistortion scaling

// Distortion coefficients
cv::Vec4d distortion_coeffs = {
    -0.05611147,  // k1
    -0.05377447,  // k2
     0.0115717,   // k3
     0.0030788    // k4
};

#define IMAGE_BACK_PIXEL_Y 643   // Back image Y offset
#define IMAGE_RIGHT_PIXEL_X 398  // Right image X offset

1. Replace Input Images: Place your fisheye images in assets/images/
2. Name Convention: Use front.png, back.png, left.png, right.png
3. Calibration Pattern: Ensure 2×4 grid is visible in overlapping areas
4. Adjust Parameters: Modify camera parameters if using different lenses

# OpenCV not found
sudo apt-get install libopencv-dev libopencv-contrib-dev

# CMake version too old
wget https://cmake.org/files/v3.20/cmake-3.20.0-linux-x86_64.sh

# Missing input images
cp your_images/* assets/images/

# Permission denied
chmod +x scripts/*.sh

• Ensure calibration board has high contrast
• Check that pattern is within valid detection region
• Verify image quality and lighting conditions

Enable debug output by modifying the source code:

#define DEBUG_MODE 1  // Add this line for debug output

• Image Loading: ~10ms per image
• Undistortion: ~50ms per image
• Corner Detection: ~100-200ms per image
• Stitching: ~100ms total
• Overall: ~1-2 seconds for complete pipeline

• Peak Memory: ~30-40MB
• Input Images: ~5MB total
• Intermediate Results: ~20MB
• Output Images: ~2-3MB

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

• OpenCV community for excellent computer vision library
• Contributors to fisheye camera calibration algorithms
• Automotive industry standards for AVM system requirements

• Documentation: Check docs/ directory for detailed guides