void Fit(const std::vector<size_t> &samples, std::vector<Model> *models) const {
const Mat2X x = ExtractColumns(x_camera_, samples);
const Mat3X X = ExtractColumns(X_, samples);
Mat34 P;
Mat3 R;
Vec3 t;
if (EuclideanResectionEPnP(x, X, &R, &t))
{
P_From_KRt(K_, R, t, &P);
models->push_back(P);
}
}
void Fit(const std::vector<size_t> &samples, std::vector<Vec2> *lines) const {
assert(samples.size() >= (unsigned int)MINIMUM_SAMPLES);
// Standard least squares solution.
const Mat2X sampled_xs = ExtractColumns(xs_, samples);
LineSolver::Solve(sampled_xs, lines);
}
void Fit
(
const std::vector<size_t> &samples,
std::vector<ModelArg> *models
)
const
{
const Mat pt2d = ExtractColumns(this->x1_, samples);
const Mat pt3d = ExtractColumns(this->x2_, samples);
assert(2 == pt2d.rows());
assert(3 == pt3d.rows());
assert(SolverArg::MINIMUM_SAMPLES <= pt2d.cols());
assert(pt2d.cols() == pt3d.cols());
SolverArg::Solve(pt2d, pt3d, models);
}
void Fit(const std::vector<size_t> &samples, std::vector<Model> *models) const {
const Mat x1 = ExtractColumns(x1_, samples);
const Mat x2 = ExtractColumns(x2_, samples);
Solver::Solve(x1, x2, models);
}
