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diff --git a/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp b/src/Simplex_tree/example/cech_complex_cgal_mini_sphere_3d.cpp
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+/*    This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+ *    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+ *    Author(s):       Vincent Rouvreau
+ *
+ *    Copyright (C) 2017 Inria
+ *
+ *    Modification(s):
+ *      - YYYY/MM Author: Description of the modification
+ */
+
+#include <gudhi/graph_simplicial_complex.h>
+#include <gudhi/distance_functions.h>
+#include <gudhi/Simplex_tree.h>
+#include <gudhi/Points_off_io.h>
+
+#include <CGAL/Epick_d.h>
+#include <CGAL/Min_sphere_of_spheres_d.h>
+#include <CGAL/Min_sphere_of_points_d_traits_d.h>
+
+#include <boost/program_options.hpp>
+
+#include <string>
+#include <vector>
+#include <limits>   // infinity
+#include <utility>  // for pair
+#include <map>
+
+// -------------------------------------------------------------------------------
+// cech_complex_cgal_mini_sphere_3d is an example of each step that is required to
+// build a Cech over a Simplex_tree. Please refer to cech_persistence to see
+// how to do the same thing with the Cech_complex wrapper for less detailed
+// steps.
+// -------------------------------------------------------------------------------
+
+// Types definition
+using Simplex_tree = Gudhi::Simplex_tree<>;
+using Vertex_handle = Simplex_tree::Vertex_handle;
+using Simplex_handle = Simplex_tree::Simplex_handle;
+using Filtration_value = Simplex_tree::Filtration_value;
+using Siblings = Simplex_tree::Siblings;
+using Graph_t = boost::adjacency_list<boost::vecS, boost::vecS, boost::directedS,
+                                      boost::property<Gudhi::vertex_filtration_t, Filtration_value>,
+                                      boost::property<Gudhi::edge_filtration_t, Filtration_value> >;
+using Edge_t = std::pair<Vertex_handle, Vertex_handle>;
+
+using Kernel = CGAL::Epick_d<CGAL::Dimension_tag<3> >;
+using Point = Kernel::Point_d;
+using Traits = CGAL::Min_sphere_of_points_d_traits_d<Kernel, Filtration_value, 3>;
+using Min_sphere = CGAL::Min_sphere_of_spheres_d<Traits>;
+
+using Points_off_reader = Gudhi::Points_off_reader<Point>;
+
+class Cech_blocker {
+ public:
+  bool operator()(Simplex_handle sh) {
+    std::vector<Point> points;
+#if DEBUG_TRACES
+    std::cout << "Cech_blocker on [";
+#endif  // DEBUG_TRACES
+    for (auto vertex : simplex_tree_.simplex_vertex_range(sh)) {
+      points.push_back(point_cloud_[vertex]);
+#if DEBUG_TRACES
+      std::cout << vertex << ", ";
+#endif  // DEBUG_TRACES
+    }
+    Min_sphere ms(points.begin(), points.end());
+    Filtration_value radius = ms.radius();
+#if DEBUG_TRACES
+    std::cout << "] - radius = " << radius << " - returns " << (radius > threshold_) << std::endl;
+#endif  // DEBUG_TRACES
+    simplex_tree_.assign_filtration(sh, radius);
+    return (radius > threshold_);
+  }
+  Cech_blocker(Simplex_tree& simplex_tree, Filtration_value threshold, const std::vector<Point>& point_cloud)
+      : simplex_tree_(simplex_tree), threshold_(threshold), point_cloud_(point_cloud) {}
+
+ private:
+  Simplex_tree simplex_tree_;
+  Filtration_value threshold_;
+  std::vector<Point> point_cloud_;
+};
+
+template <typename InputPointRange>
+Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold);
+
+void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max);
+
+int main(int argc, char* argv[]) {
+  std::string off_file_points;
+  Filtration_value threshold;
+  int dim_max;
+
+  program_options(argc, argv, off_file_points, threshold, dim_max);
+
+  // Extract the points from the file filepoints
+  Points_off_reader off_reader(off_file_points);
+
+  // Compute the proximity graph of the points
+  Graph_t prox_graph = compute_proximity_graph(off_reader.get_point_cloud(), threshold);
+
+  // Min_sphere sph1(off_reader.get_point_cloud()[0], off_reader.get_point_cloud()[1], off_reader.get_point_cloud()[2]);
+  // Construct the Rips complex in a Simplex Tree
+  Simplex_tree st;
+  // insert the proximity graph in the simplex tree
+  st.insert_graph(prox_graph);
+  // expand the graph until dimension dim_max
+  st.expansion_with_blockers(dim_max, Cech_blocker(st, threshold, off_reader.get_point_cloud()));
+
+  std::cout << "The complex contains " << st.num_simplices() << " simplices \n";
+  std::cout << "   and has dimension " << st.dimension() << " \n";
+
+  // Sort the simplices in the order of the filtration
+  st.initialize_filtration();
+
+#if DEBUG_TRACES
+  std::cout << "********************************************************************\n";
+  // Display the Simplex_tree - Can not be done in the middle of 2 inserts
+  std::cout << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n";
+  std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n";
+  for (auto f_simplex : st.filtration_simplex_range()) {
+    std::cout << "   "
+              << "[" << st.filtration(f_simplex) << "] ";
+    for (auto vertex : st.simplex_vertex_range(f_simplex)) {
+      std::cout << static_cast<int>(vertex) << " ";
+    }
+    std::cout << std::endl;
+  }
+#endif  // DEBUG_TRACES
+  return 0;
+}
+
+void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max) {
+  namespace po = boost::program_options;
+  po::options_description hidden("Hidden options");
+  hidden.add_options()("input-file", po::value<std::string>(&off_file_points),
+                       "Name of an OFF file containing a 3d point set.\n");
+
+  po::options_description visible("Allowed options", 100);
+  visible.add_options()("help,h", "produce help message")(
+      "max-edge-length,r",
+      po::value<Filtration_value>(&threshold)->default_value(std::numeric_limits<Filtration_value>::infinity()),
+      "Maximal length of an edge for the Cech complex construction.")(
+      "cpx-dimension,d", po::value<int>(&dim_max)->default_value(1),
+      "Maximal dimension of the Cech complex we want to compute.");
+
+  po::positional_options_description pos;
+  pos.add("input-file", 1);
+
+  po::options_description all;
+  all.add(visible).add(hidden);
+
+  po::variables_map vm;
+  po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm);
+  po::notify(vm);
+
+  if (vm.count("help") || !vm.count("input-file")) {
+    std::cout << std::endl;
+    std::cout << "Construct a Cech complex defined on a set of input points.\n \n";
+
+    std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl;
+    std::cout << visible << std::endl;
+    exit(-1);
+  }
+}
+
+/** Output the proximity graph of the points.
+ *
+ * If points contains n elements, the proximity graph is the graph
+ * with n vertices, and an edge [u,v] iff the distance function between
+ * points u and v is smaller than threshold.
+ *
+ * The type PointCloud furnishes .begin() and .end() methods, that return
+ * iterators with value_type Point.
+ */
+template <typename InputPointRange>
+Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold) {
+  std::vector<Edge_t> edges;
+  std::vector<Filtration_value> edges_fil;
+
+  Kernel k;
+  Vertex_handle idx_u, idx_v;
+  Filtration_value fil;
+  idx_u = 0;
+  for (auto it_u = points.begin(); it_u != points.end(); ++it_u) {
+    idx_v = idx_u + 1;
+    for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) {
+      fil = k.squared_distance_d_object()(*it_u, *it_v);
+      // For Cech Complex, threshold is a radius (distance /2)
+      fil = std::sqrt(fil) / 2.;
+      if (fil <= threshold) {
+        edges.emplace_back(idx_u, idx_v);
+        edges_fil.push_back(fil);
+      }
+    }
+    ++idx_u;
+  }
+
+  Graph_t skel_graph(edges.begin(), edges.end(), edges_fil.begin(),
+                     idx_u);  // number of points labeled from 0 to idx_u-1
+
+  auto vertex_prop = boost::get(Gudhi::vertex_filtration_t(), skel_graph);
+
+  boost::graph_traits<Graph_t>::vertex_iterator vi, vi_end;
+  for (std::tie(vi, vi_end) = boost::vertices(skel_graph); vi != vi_end; ++vi) {
+    boost::put(vertex_prop, *vi, 0.);
+  }
+
+  return skel_graph;
+}