diff --git a/examples/SelfCalibrationExample.cpp b/examples/SelfCalibrationExample.cpp
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+++ b/examples/SelfCalibrationExample.cpp
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+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file    SelfCalibrationExample.cpp
+ * @brief   Based on VisualSLAMExample, but with unknown (yet fixed) calibration.
+ * @author  Frank Dellaert
+ */
+
+/*
+ * See the detailed documentation in Visual SLAM.
+ * The only documentation below with deal with the self-calibration.
+ */
+
+// For loading the data
+#include "visualSLAMdata.h"
+
+// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
+#include <gtsam/geometry/Point2.h>
+
+// Inference and optimization
+#include <gtsam/inference/Symbol.h>
+#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/nonlinear/DoglegOptimizer.h>
+#include <gtsam/nonlinear/Values.h>
+
+// SFM-specific factors
+#include <gtsam/slam/PriorFactor.h>
+#include <gtsam/slam/GeneralSFMFactor.h> // does calibration !
+
+// Standard headers
+#include <vector>
+
+using namespace std;
+using namespace gtsam;
+
+/* ************************************************************************* */
+int main(int argc, char* argv[]) {
+
+  // Create the set of ground-truth
+  std::vector<gtsam::Point3> points = createPoints();
+  std::vector<gtsam::Pose3> poses = createPoses();
+
+  // Create the factor graph
+  NonlinearFactorGraph graph;
+
+  // Add a prior on pose x1.
+  noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas(Vector_(6, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1)); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
+  graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise)); // add directly to graph
+
+  // Simulated measurements from each camera pose, adding them to the factor graph
+  Cal3_S2 K(50.0, 50.0, 0.0, 50.0, 50.0);
+  noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
+  for (size_t i = 0; i < poses.size(); ++i) {
+    for (size_t j = 0; j < points.size(); ++j) {
+      SimpleCamera camera(poses[i], K);
+      Point2 measurement = camera.project(points[j]);
+      // The only real difference with the Visual SLAM example is that here we use a
+      // different factor type, that also calculates the Jacobian with respect to calibration
+      graph.push_back(GeneralSFMFactor2<Cal3_S2>(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), Symbol('K', 0)));
+    }
+  }
+
+  // Add a prior on the position of the first landmark.
+  noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
+  graph.push_back(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
+
+  // Create the initial estimate to the solution
+  // Including an estimate on the camera calibration parameters
+  Values initialEstimate;
+  initialEstimate.insert(Symbol('K', 0), Cal3_S2(60.0, 60.0, 0.0, 45.0, 45.0));
+  for (size_t i = 0; i < poses.size(); ++i)
+    initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
+  for (size_t j = 0; j < points.size(); ++j)
+    initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
+
+  /* Optimize the graph and print results */
+  Values result = DoglegOptimizer(graph, initialEstimate).optimize();
+  result.print("Final results:\n");
+
+  return 0;
+}
+/* ************************************************************************* */
+