Camera Calibration and 3D Reconstruction

The Camera Calibration and 3D Reconstruction (calib3d) module provides algorithms for camera calibration, stereo vision, 3D reconstruction, and geometric transformations for computer vision applications.

Overview

From opencv2/calib3d.hpp:54-277, detailed mathematical background:

The functions in this section use a pinhole camera model with lens distortion for camera calibration, stereo calibration and rectification, 3D reconstruction from stereo, and pose estimation.

Camera Calibration

Determine intrinsic and extrinsic camera parameters

Stereo Vision

Calibrate stereo camera pairs and rectify images

3D Reconstruction

Reconstruct 3D points from multiple views

Pose Estimation

Estimate object position and orientation

Pinhole Camera Model

From calib3d.hpp:64-72, the fundamental projection equation: [ s \begin u \ v \ 1 \end = \mathbf \begin \mathbf | \mathbf \end \begin X_w \ Y_w \ Z_w \ 1 \end ] Where:

A - Camera intrinsic matrix (focal length, principal point)
R - Rotation matrix (3x3)
t - Translation vector (3x1)
(X_w, Y_w, Z_w) - 3D world coordinates
(u, v) - 2D image coordinates

Camera Intrinsic Matrix

From calib3d.hpp:79-83: [ \mathbf = \begin f_x & 0 & c_x \ 0 & f_y & c_y \ 0 & 0 & 1 \end ]

f_x, f_y - Focal lengths in pixel units
c_x, c_y - Principal point (optical center)

Lens Distortion

From calib3d.hpp:225-264, real lenses have distortion:

Distortion Coefficients

// Distortion vector (OpenCV format)
vector<double> distCoeffs = {
    k1, k2, p1, p2, k3,  // Standard 5 parameters
    k4, k5, k6,          // Optional: radial distortion
    s1, s2, s3, s4       // Optional: thin prism
};

k1, k2, k3, k4, k5, k6 - Radial distortion coefficients
p1, p2 - Tangential distortion coefficients
s1, s2, s3, s4 - Thin prism distortion coefficients

Distortion Model

From calib3d.hpp:228-244:

x'' = x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x'y' + p_2(r^2 + 2x'^2)

y'' = y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1(r^2 + 2y'^2) + 2p_2 x'y'

Where ( r^2 = x’^2 + y’^2 )

Camera Calibration

Example from samples/cpp/calibration.cpp:

Chessboard Calibration

#include <opencv2/calib3d.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

using namespace cv;
using namespace std;

int main() {
    // Calibration pattern size (inner corners)
    Size boardSize(9, 6);  // 9x6 chessboard
    float squareSize = 25.0;  // mm
    
    // Collect calibration images
    vector<vector<Point2f>> imagePoints;
    vector<vector<Point3f>> objectPoints;
    
    VideoCapture cap(0);
    Mat frame, gray;
    
    cout << "Press SPACE to capture, ESC to finish\n";
    
    while (cap.read(frame)) {
        cvtColor(frame, gray, COLOR_BGR2GRAY);
        
        // Find chessboard corners
        vector<Point2f> corners;
        bool found = findChessboardCorners(
            gray, boardSize, corners,
            CALIB_CB_ADAPTIVE_THRESH |
            CALIB_CB_NORMALIZE_IMAGE |
            CALIB_CB_FAST_CHECK
        );
        
        if (found) {
            // Refine corner positions
            cornerSubPix(gray, corners, Size(11, 11),
                        Size(-1, -1),
                        TermCriteria(TermCriteria::EPS +
                                    TermCriteria::MAX_ITER,
                                    30, 0.1));
            
            // Draw corners
            drawChessboardCorners(frame, boardSize,
                                corners, found);
        }
        
        imshow("Calibration", frame);
        
        int key = waitKey(30);
        if (key == ' ' && found) {
            // Capture image
            imagePoints.push_back(corners);
            
            // Generate 3D object points
            vector<Point3f> obj;
            for (int i = 0; i < boardSize.height; i++) {
                for (int j = 0; j < boardSize.width; j++) {
                    obj.push_back(Point3f(
                        j * squareSize,
                        i * squareSize,
                        0
                    ));
                }
            }
            objectPoints.push_back(obj);
            
            cout << "Captured " << imagePoints.size()
                 << " images\n";
        }
        else if (key == 27) break;  // ESC
    }
    
    // Calibrate camera
    if (imagePoints.size() >= 10) {
        Mat cameraMatrix, distCoeffs;
        vector<Mat> rvecs, tvecs;
        
        double rms = calibrateCamera(
            objectPoints,
            imagePoints,
            gray.size(),
            cameraMatrix,
            distCoeffs,
            rvecs,
            tvecs
        );
        
        cout << "\nCalibration complete!\n";
        cout << "RMS error: " << rms << "\n";
        cout << "Camera matrix:\n" << cameraMatrix << "\n";
        cout << "Distortion coefficients:\n"
             << distCoeffs << "\n";
        
        // Save calibration
        FileStorage fs("calibration.yml", FileStorage::WRITE);
        fs << "camera_matrix" << cameraMatrix;
        fs << "distortion_coefficients" << distCoeffs;
        fs << "image_width" << gray.cols;
        fs << "image_height" << gray.rows;
        fs << "rms_error" << rms;
        fs.release();
        
        cout << "Saved to calibration.yml\n";
    }
    else {
        cout << "Not enough images for calibration\n";
    }
    
    return 0;
}

Calibration Flags

// Calibration flags
int flags = 0;
flags |= CALIB_FIX_ASPECT_RATIO;    // Fix fx/fy ratio
flags |= CALIB_ZERO_TANGENT_DIST;   // Assume p1=p2=0
flags |= CALIB_FIX_PRINCIPAL_POINT; // Fix cx, cy at center
flags |= CALIB_FIX_K1;              // Fix k1=0
flags |= CALIB_FIX_K2;              // Fix k2=0
flags |= CALIB_FIX_K3;              // Fix k3=0
flags |= CALIB_RATIONAL_MODEL;      // Enable k4, k5, k6

calibrateCamera(objectPoints, imagePoints, imageSize,
               cameraMatrix, distCoeffs, rvecs, tvecs, flags);

Undistortion

Undistort Images

// Load calibration
FileStorage fs("calibration.yml", FileStorage::READ);
Mat cameraMatrix, distCoeffs;
fs["camera_matrix"] >> cameraMatrix;
fs["distortion_coefficients"] >> distCoeffs;

// Undistort image
Mat img = imread("distorted.jpg");
Mat undistorted;
undistort(img, undistorted, cameraMatrix, distCoeffs);

// Or get optimal camera matrix
Mat newCameraMatrix = getOptimalNewCameraMatrix(
    cameraMatrix, distCoeffs, img.size(), 1.0);

undistort(img, undistorted, cameraMatrix,
         distCoeffs, newCameraMatrix);

Remap for Efficiency

// Compute undistortion maps once
Mat map1, map2;
initUndistortRectifyMap(
    cameraMatrix, distCoeffs,
    Mat(),  // No rectification
    newCameraMatrix,
    imageSize,
    CV_16SC2,
    map1, map2
);

// Apply to multiple images efficiently
for (const auto& img : images) {
    Mat undistorted;
    remap(img, undistorted, map1, map2, INTER_LINEAR);
}

Stereo Calibration

Calibrate Stereo Pair

// Collect image points from both cameras
vector<vector<Point2f>> leftPoints, rightPoints;
vector<vector<Point3f>> objectPoints;
// ... collect points from stereo pairs ...

// Individual camera matrices and distortion
Mat K1, K2, D1, D2;
Mat R, T, E, F;  // Stereo parameters

// Stereo calibration
double rms = stereoCalibrate(
    objectPoints,
    leftPoints,
    rightPoints,
    K1, D1,  // Left camera
    K2, D2,  // Right camera
    imageSize,
    R,       // Rotation between cameras
    T,       // Translation between cameras
    E,       // Essential matrix
    F,       // Fundamental matrix
    CALIB_FIX_INTRINSIC  // Use pre-calibrated cameras
);

cout << "Stereo calibration RMS: " << rms << endl;
cout << "Baseline: " << norm(T) << " mm\n";

Stereo Rectification

// Compute rectification transforms
Mat R1, R2, P1, P2, Q;
Rect validRoi[2];

stereoRectify(
    K1, D1,  // Left camera
    K2, D2,  // Right camera
    imageSize,
    R, T,    // Stereo parameters
    R1, R2,  // Output: rectification rotations
    P1, P2,  // Output: projection matrices
    Q,       // Output: disparity-to-depth mapping
    CALIB_ZERO_DISPARITY,
    1.0,     // Alpha (0=crop, 1=all pixels)
    imageSize,
    &validRoi[0],
    &validRoi[1]
);

// Create rectification maps
Mat map1L, map2L, map1R, map2R;

initUndistortRectifyMap(K1, D1, R1, P1, imageSize,
                        CV_16SC2, map1L, map2L);
initUndistortRectifyMap(K2, D2, R2, P2, imageSize,
                        CV_16SC2, map1R, map2R);

// Rectify stereo pair
Mat leftImg, rightImg;
Mat rectLeft, rectRight;

remap(leftImg, rectLeft, map1L, map2L, INTER_LINEAR);
remap(rightImg, rectRight, map1R, map2R, INTER_LINEAR);

// Now rectLeft and rectRight have aligned epipolar lines

Disparity and Depth

Stereo Matching

#include <opencv2/calib3d.hpp>
#include <opencv2/imgproc.hpp>

// Create stereo matcher
Ptr<StereoBM> stereo = StereoBM::create(16*5, 21);

// Or use StereoSGBM for better quality
Ptr<StereoSGBM> stereo = StereoSGBM::create(
    0,        // minDisparity
    16*5,     // numDisparities (must be divisible by 16)
    21,       // blockSize
    8*21*21,  // P1
    32*21*21, // P2
    1,        // disp12MaxDiff
    63,       // preFilterCap
    10,       // uniquenessRatio
    100,      // speckleWindowSize
    32,       // speckleRange
    StereoSGBM::MODE_SGBM_3WAY
);

// Compute disparity
Mat disparity;
stereo->compute(rectLeft, rectRight, disparity);

// Normalize for visualization
Mat disp8;
disparity.convertTo(disp8, CV_8U, 255.0/(16*5*16));
imshow("Disparity", disp8);

Reconstruct 3D Points

// Compute 3D points from disparity
Mat points3D;
reprojectImageTo3D(disparity, points3D, Q);

// Access 3D coordinates
for (int y = 0; y < points3D.rows; y++) {
    for (int x = 0; x < points3D.cols; x++) {
        Vec3f point = points3D.at<Vec3f>(y, x);
        float X = point[0];
        float Y = point[1];
        float Z = point[2];
        
        // Filter invalid points
        if (abs(Z) < 10000) {
            // Valid 3D point
        }
    }
}

Pose Estimation

solvePnP - Estimate Camera Pose

// Known 3D object points
vector<Point3f> objectPoints = {
    Point3f(0, 0, 0),
    Point3f(100, 0, 0),
    Point3f(100, 100, 0),
    Point3f(0, 100, 0)
};

// Corresponding 2D image points
vector<Point2f> imagePoints = {
    Point2f(234, 456),
    Point2f(567, 445),
    Point2f(589, 234),
    Point2f(245, 267)
};

// Camera calibration
Mat cameraMatrix, distCoeffs;
// ... load calibration ...

// Solve for pose
Mat rvec, tvec;
bool success = solvePnP(
    objectPoints,
    imagePoints,
    cameraMatrix,
    distCoeffs,
    rvec,  // Output: rotation vector
    tvec,  // Output: translation vector
    false, // useExtrinsicGuess
    SOLVEPNP_ITERATIVE  // Method
);

if (success) {
    cout << "Rotation: " << rvec.t() << "\n";
    cout << "Translation: " << tvec.t() << "\n";
    
    // Convert rotation vector to matrix
    Mat R;
    Rodrigues(rvec, R);
}

PnP Methods

enum SolvePnPMethod {
    SOLVEPNP_ITERATIVE,  // Iterative method
    SOLVEPNP_EPNP,       // Efficient PnP
    SOLVEPNP_P3P,        // 3-point algorithm
    SOLVEPNP_DLS,        // Direct Least Squares
    SOLVEPNP_UPNP,       // Unified PnP
    SOLVEPNP_AP3P,       // Alternative P3P
    SOLVEPNP_IPPE,       // Infinitesimal Plane-based Pose
    SOLVEPNP_IPPE_SQUARE // IPPE for square markers
};

Draw 3D Axes

void drawAxes(Mat& img, const Mat& cameraMatrix,
              const Mat& distCoeffs,
              const Mat& rvec, const Mat& tvec,
              float length) {
    // 3D axis points
    vector<Point3f> axisPoints = {
        Point3f(0, 0, 0),      // Origin
        Point3f(length, 0, 0), // X axis
        Point3f(0, length, 0), // Y axis
        Point3f(0, 0, length)  // Z axis
    };
    
    // Project to 2D
    vector<Point2f> imagePoints;
    projectPoints(axisPoints, rvec, tvec,
                 cameraMatrix, distCoeffs, imagePoints);
    
    // Draw axes
    line(img, imagePoints[0], imagePoints[1],
         Scalar(0, 0, 255), 3);  // X - Red
    line(img, imagePoints[0], imagePoints[2],
         Scalar(0, 255, 0), 3);  // Y - Green
    line(img, imagePoints[0], imagePoints[3],
         Scalar(255, 0, 0), 3);  // Z - Blue
}

Homography

Find Homography

// Match points between two images
vector<Point2f> srcPoints, dstPoints;
// ... find corresponding points ...

// Compute homography
Mat H = findHomography(srcPoints, dstPoints, RANSAC, 3.0);

// Warp image
Mat warped;
warpPerspective(srcImage, warped, H, dstImage.size());

Decompose Homography

// Decompose into rotation and translation
vector<Mat> rotations, translations, normals;
int solutions = decomposeHomographyMat(
    H, cameraMatrix,
    rotations, translations, normals
);

cout << "Found " << solutions << " solutions\n";

Triangulation

// Points from two calibrated cameras
vector<Point2f> points1, points2;
Mat P1, P2;  // Projection matrices

// Triangulate points
Mat points4D;
triangulatePoints(P1, P2, points1, points2, points4D);

// Convert from homogeneous coordinates
vector<Point3f> points3D;
for (int i = 0; i < points4D.cols; i++) {
    float w = points4D.at<float>(3, i);
    Point3f pt(
        points4D.at<float>(0, i) / w,
        points4D.at<float>(1, i) / w,
        points4D.at<float>(2, i) / w
    );
    points3D.push_back(pt);
}

Best Practices

Calibration Quality:

Use at least 10-20 images from different angles
Cover the entire image area with calibration pattern
RMS error should be < 1 pixel for good calibration
Check reprojection errors for outliers

Calibration Pattern:

Chessboard is most common and reliable
Ensure pattern is perfectly flat
Use high-quality printing
Good lighting without glare

Stereo Vision:

Baseline (distance between cameras) affects depth range
Larger baseline = better depth accuracy at distance
Cameras should be well-aligned (< 5° rotation)
Synchronized capture for moving scenes

Performance:

// Cache undistortion maps
Mat map1, map2;
initUndistortRectifyMap(K, D, Mat(), K, size,
                        CV_16SC2, map1, map2);

// Reuse for all images
for (auto& img : images) {
    remap(img, undistorted, map1, map2, INTER_LINEAR);
}

Features 2D - Feature detection for matching
Image Processing - Image transformations
Video Analysis - Optical flow for tracking

Source Reference

Main header: ~/workspace/source/modules/calib3d/include/opencv2/calib3d.hpp Examples:

samples/cpp/calibration.cpp - Camera calibration
samples/cpp/stereo_calib.cpp - Stereo calibration
samples/cpp/stereo_match.cpp - Stereo matching

Getting Started

Core Concepts

Modules

Tutorials

Language Bindings

Platform Support

Camera Calibration and 3D Reconstruction

Overview

Camera Calibration

Stereo Vision

3D Reconstruction

Pose Estimation

Pinhole Camera Model

Camera Intrinsic Matrix

Lens Distortion

Distortion Coefficients

Distortion Model

Camera Calibration

Chessboard Calibration

Calibration Flags

Undistortion

Undistort Images

Remap for Efficiency

Stereo Calibration

Calibrate Stereo Pair

Stereo Rectification

Disparity and Depth

Stereo Matching

Reconstruct 3D Points

Pose Estimation

solvePnP - Estimate Camera Pose

PnP Methods

Draw 3D Axes

Homography

Find Homography

Decompose Homography

Triangulation

Best Practices

Source Reference

Getting Started

Core Concepts

Modules

Tutorials

Language Bindings

Platform Support

​Overview

Camera Calibration

Stereo Vision

3D Reconstruction

Pose Estimation

​Pinhole Camera Model

​Camera Intrinsic Matrix

​Lens Distortion

​Distortion Coefficients

​Distortion Model

​Camera Calibration

​Chessboard Calibration

​Calibration Flags

​Undistortion

​Undistort Images

​Remap for Efficiency

​Stereo Calibration

​Calibrate Stereo Pair

​Stereo Rectification

​Disparity and Depth

​Stereo Matching

​Reconstruct 3D Points

​Pose Estimation

​solvePnP - Estimate Camera Pose

​PnP Methods

​Draw 3D Axes

​Homography

​Find Homography

​Decompose Homography

​Triangulation

​Best Practices

​Related Modules

​Source Reference

Overview

Pinhole Camera Model

Camera Intrinsic Matrix

Lens Distortion

Distortion Coefficients

Distortion Model

Camera Calibration

Chessboard Calibration

Calibration Flags

Undistortion

Undistort Images

Remap for Efficiency

Stereo Calibration

Calibrate Stereo Pair

Stereo Rectification

Disparity and Depth

Stereo Matching

Reconstruct 3D Points

Pose Estimation

solvePnP - Estimate Camera Pose

PnP Methods

Draw 3D Axes

Homography

Find Homography

Decompose Homography

Triangulation

Best Practices

Related Modules

Source Reference