int searchModelWithMEstimator(const Kernel &kernel,
int maxNbIterations,
typename Kernel::Model* bestModel,
double *RMS = 0,
double *sigmaMAD_p = 0)
{
assert(bestModel);
const int N = (int)kernel.NumSamples();
const int m = (int)Kernel::MinimumSamples();
// Test if we have sufficient points for the kernel.
if (N < m) {
return 0;
} else if (N == m) {
bool ok = searchModel_minimalSamples(kernel, bestModel, 0, RMS);
return ok ? 1 : 0;
}
// Compute a first model on all samples with least squares
int hasModel = kernel.ComputeModelFromAllSamples(bestModel);
if (!hasModel) {
return 0;
}
InliersVec isInlier(N, true);
int nbSuccessfulIterations = kernel.MEstimator(*bestModel, isInlier, maxNbIterations, bestModel, RMS, sigmaMAD_p);
if (RMS) {
*RMS = kernel.ScalarUnormalize(*RMS);
}
kernel.Unnormalize(bestModel);
return nbSuccessfulIterations;
} // searchModelWithMEstimator
bool searchModelLS(const Kernel &kernel,
typename Kernel::Model* bestModel,
double *RMS = 0)
{
assert(bestModel);
const int N = (int)kernel.NumSamples();
const int m = (int)Kernel::MinimumSamples();
// Test if we have sufficient points for the kernel.
if (N < m) {
return 0;
} else if (N == m) {
return searchModel_minimalSamples(kernel, bestModel, 0, RMS);
}
bool ok = kernel.ComputeModelFromAllSamples(bestModel);
if (RMS) {
InliersVec isInlier(N);
int nInliers = kernel.ComputeInliersForModel(*bestModel, &isInlier, RMS);
(void)nInliers;
}
if (RMS) {
*RMS = kernel.ScalarUnormalize(*RMS);
}
kernel.Unnormalize(bestModel);
return ok;
} // searchModelWithMEstimator