Depth map from calibrated image and triangular mesh using OpenCV and Matlab

Question

I want to extract a depth map from a calibrated image and triangular mesh using OpenCV called from Matlab 2014b (using the OpenCV bindings). I am a regular user of Matlab but am new to OpenCV. I have the following inputs:

im - an undistorted RGB image of the scene

or - camera position vector

R - rotation matrix describing camera pose

points - nx3 triangular mesh vertex list representing the scene

faces - mx3 triangular mesh face list

EFL - image effective focal length in pixels

I have written a native Matlab ray tracing engine to extract a depth map from these inputs, but this is quite slow and suffers from high reprojection errors (I want to compare the results from OpenCV functions to my own to establish if these errors relate to my implementation or just a camera calibration inaccuracies).

How can a depth map be obtained from these inputs using OpenCV called from Matlab?

Any help would be greatly appreciated

Thanks

Thomas

kmac kmac · Accepted Answer · 2015-11-17T05:08:24

Proposed strategy

You could project the vertices from your mesh into 2D pixel coordinates (using your calibrated camera model). Then for each face, you can find all of the pixel centres (lattice points) contained in the 2D triangle formed by its projected vertices. You may have to keep track of which triangle is the nearest in the case of overlap. Now you know which face corresponds to each pixel. This should be very fast unless your mesh is much higher resolution than your image.

You can then find the 3D ray corresponding to each pixel using the camera model, and intersect the ray with the known face for that pixel to calculate the depth (sounds like you already did this part). This shouldn't take too long either, now that you know the plane.

More info on the camera projection

OpenCV has a good resource on using the camera model (below). Basically, you can project 3D point M' to pixel coordinate m'; this is how you project your vertices to pixel positions. Going the other direction, scale is unrecoverable -- you get the ray M'/s rather than the point M'. The depth you're looking for is s, which is the 3D point's Z coordinate in the camera frame. If your mesh is in a camera-centric frame (X right, Y down, Z out), R = Identity and t = 0. If it's not, [R|t] transforms it to be.

Compact

Expanding each factor lets us see the makeup of the matrices.

Expanded

The code that you suggested below uses OpenCV's projectPoints function, which implements the above equation plus some distortion calibration (see main OpenCV reference). You have to populate the matrices and it multiplies them. An alternative example for projectPoints is available on GitHub, and I believe this same example is discussed in this SO question.

Code suggested by asker

Apparently the following code does the job. I may need some time to pick through it given that my C++ knowledge is practically zero (I realise that it is commented out BTW):

       //CString str;
       //cv::Mat CamMatrix(3, 3, CV_64F);
       //cv::Mat distCoeffs(5, 1, CV_64F);
       //m_CamCalib.GetOpenCVInfo(&CamMatrix, &distCoeffs);
       //vector<Point3d> GCP_Points;
       //vector<Point2d> Image_Points;
       //cv::Mat RVecs(3, 3, CV_64F); // rotation matrix
       //cv::Mat TranRVecs(3, 3, CV_64F); // rotation matrix
       //cv::Mat TVecs(3, 1, CV_64F); // translation vector
       //RVecs.at<double>(0, 0) = m_CamPosMtrx.m_pMtrx[0];
       //RVecs.at<double>(1, 0) = m_CamPosMtrx.m_pMtrx[1];
       //RVecs.at<double>(2, 0) = m_CamPosMtrx.m_pMtrx[2];

       //RVecs.at<double>(0, 1) = m_CamPosMtrx.m_pMtrx[4];
       //RVecs.at<double>(1, 1) = m_CamPosMtrx.m_pMtrx[5];
       //RVecs.at<double>(2, 1) = m_CamPosMtrx.m_pMtrx[6];

       //RVecs.at<double>(0, 2) = m_CamPosMtrx.m_pMtrx[8];
       //RVecs.at<double>(1, 2) = m_CamPosMtrx.m_pMtrx[9];
       //RVecs.at<double>(2, 2) = m_CamPosMtrx.m_pMtrx[10];
       //transpose(RVecs, TranRVecs);
       //TVecs.at<double>(0, 0) = 0;
       //TVecs.at<double>(1, 0) = 0;
       //TVecs.at<double>(2, 0) = 0;
       //GCP_Points.push_back(Point3d((x - m_CamPosMtrx.m_pMtrx[12]), (y - m_CamPosMtrx.m_pMtrx[13]), (z - m_CamPosMtrx.m_pMtrx[14])));
       //Image_Points.push_back(Point2d(0, 0));
       //projectPoints(GCP_Points, TranRVecs, TVecs, CamMatrix, distCoeffs, Image_Points);

/bool CCameraCalibration::GetOpenCVInfo(Mat * cameraMatrix, Mat * distCoeffs)
//{
//            int i,j;
//            Mat projMatrix;
//            CMatrix4x4 m1;
//            if(cameraMatrix->rows==0) cameraMatrix->create(3,3, CV_64F);
//            if(distCoeffs->rows==0) distCoeffs->create(5, 1, CV_64F);
//            for(i=0;i<3;i++)
//            for(j=0;j<3;j++){
//                   cameraMatrix->at<double>(i,j)=m_pCameraMatrix[i][j];
//            }
//            for(i=0;i<5;i++)
//                   distCoeffs->at<double>(i,0)=m_pCoefficients[i];
//     return false;
//}

Depth map from calibrated image and triangular mesh using OpenCV and Matlab

1 Answers