1

I'm a new learner to Ceres Solver, when adding the residualblock using 

problem.AddResidualBlock( new ceres::AutoDiffCostFunction<Opt, 1, 6> (new Opt(Pts[i][j].x, Pts[i][j].y, Pts[i][j].z, Ns[i].at<double>(0, 0), Ns[i].at<double>(1, 0), Ns[i].at<double>(2, 0), Ds[i], weights[i]) ),
                      NULL,
                              param );

where param is double[6];

struct Opt
{
const double ptX, ptY, ptZ, nsX, nsY, nsZ, ds, w;

Opt( double ptx, double pty, double ptz, double nsx, double nsy, double nsz, double ds1, double w1):
         ptX(ptx), ptY(pty), ptZ(ptz), nsX(nsx), nsY(nsy), nsZ(nsz), ds(ds1), w(w1) {}

template<typename T>
bool operator()(const T* const x, T* residual) const
{
    Mat R(3, 3, CV_64F), r(1, 3, CV_64F);
    Mat inverse(3,3, CV_64F);
    T newP[3];
    T xyz[3];
        for (int i = 0; i < 3; i++){
            r.at<T>(i) = T(x[i]);
            cout<<x[i]<<endl;
            }
    Rodrigues(r, R);
    inverse = R.inv();
    newP[0]=T(ptX)-x[3];
    newP[1]=T(ptY)-x[4];
    newP[2]=T(ptZ)-x[5];
    xyz[0]= inverse.at<T>(0, 0)*newP[0] + inverse.at<T>(0, 1)*newP[1] + inverse.at<T>(0, 2)*newP[2];
    xyz[1] = inverse.at<T>(1, 0)*newP[0] + inverse.at<T>(1, 1)*newP[1] + inverse.at<T>(1, 2)*newP[2];
    xyz[2] = inverse.at<T>(2, 0)*newP[0] + inverse.at<T>(2, 1)*newP[1] + inverse.at<T>(2, 2)*newP[2];
    T ds1 = T(nsX) * xyz[0] + T(nsY) * xyz[1] + T(nsZ) * xyz[2];
    residual[0] = (ds1 - T(ds)) * T(w);
}
};

but when I output the x[0], I got this:

[-1.40926 ; 1, 0, 0, 0, 0, 0]

after I change the type of the x to double

I got this error :

note:   no known conversion for argument 1 from ‘const ceres::Jet<double, 6>* const’ to ‘const double*’

in 

bool operator()(const double* const x, double* residual) const

what's wrong with my codes? Thanks a lot!

zhouuu
  • 13
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2 Answers2

0

The automatic derivatives (AutoDiff) needs a templated cost function to keep track of the operations.

Please take a look at the ceres documentation (http://ceres-solver.org/nnls_modeling.html#autodiffcostfunction). There are a lot of nice examples too. I used them as starting point for my first ceres experiments.

I'm not sure if you can use ceres cost functions with OpenCV functions. In most cases Eigen is used to make the cost function. Ceres comes with a lot of "ready-to-use" components for cost functions like yours.

Rasmus Debitsch
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0

I am guessing you are using cv::Mat.

The reason the functor is templated is because Ceres evaluates it using doubles when it needs just the residuals, and evaluates with ceres:Jet objects when it needs to compute the Jacobian. So your attempt to fill r as

for (int i = 0; i < 3; i++){
    r.at<T>(i) = T(x[i]);
    cout<<x[i]<<endl;
}

are trying to convert a Jet into a double. Which is what the compiler is correctly complaining about.

you can re-write your code as (I have not compiled it, so there maybe a minor typo or two).

template<typename T>
bool operator()(const T* const x, T* residual) const {
    const T inverse_rotation[3] = {-x[0], -x[1], -x[3]};
    const T newP[3] = {ptX - x[3], ptY - x[4]. ptZ - x[5]};
    T xyz[3];
    ceres::AngleAxisRotatePoint(inverse_rotation, newP, xyz);
    const T ds1 = nsX * xyz[0] + nsY * xyz[1] + nsZ * xyz[2];
    residual[0] = (ds1 - ds) * w;
    return true;
}
Sameer Agarwal
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