minor enhancement. use iota in maxconsensus and add a missing templat…

…e alias in the two view kernel.

src/openMVG/multiview/two_view_kernel.hpp

@@ -71,6 +71,8 @@ class Kernel {
   Kernel(const Mat &x1, const Mat &x2) : x1_(x1), x2_(x2) {}
   typedef SolverArg Solver;
   typedef ModelArg  Model;
+  typedef ErrorArg  ErrorT;
+
   /// The minimal number of point required for the model estimation
   enum { MINIMUM_SAMPLES = Solver::MINIMUM_SAMPLES };
   /// The number of models that the minimal solver could return.
@@ -90,7 +92,7 @@ class Kernel {
   size_t NumSamples() const {
     return x1_.cols();
   }
-  /// Compute a model on sampled point
+  /// Compute a model on sampled datum
   static void Solve(const Mat &x1, const Mat &x2, vector<Model> *models) {
     // By offering this, Kernel types can be passed to templates.
     Solver::Solve(x1, x2, models);

src/openMVG/robust_estimation/robust_estimator_MaxConsensus.hpp

@@ -51,14 +51,12 @@ typename Kernel::Model MaxConsensus(const Kernel &kernel,
 
     // In this robust estimator, the scorer always works on all the data points
     // at once. So precompute the list ahead of time.
-    std::vector<size_t> all_samples;
-    for (size_t i = 0; i < total_samples; ++i) {
-      all_samples.push_back(i);
-    }
+    std::vector<size_t> all_samples(total_samples);
+    std::iota(all_samples.begin(), all_samples.end(), 0);
 
     std::vector<size_t> sample;
     for (size_t iteration = 0;  iteration < max_iteration; ++iteration) {
-        UniformSample(min_samples, total_samples, &sample);
+      UniformSample(min_samples, total_samples, &sample);
 
         std::vector<typename Kernel::Model> models;
         kernel.Fit(sample, &models);