diff --git a/examples/SFMExample_SmartProjectionPoseFactor.cpp b/examples/SFMExample_SmartProjectionPoseFactor.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    SFMExample_SmartFactor.cpp
+ * @brief   A structure-from-motion problem on a simulated dataset, using smart projection factor
+ * @author  Luca Carlone
+ * @author  Frank Dellaert
+ */
+
+// In GTSAM, measurement functions are represented as 'factors'.
+// The factor we used here is SmartProjectionPoseFactor.
+// Every smart factor represent a single landmark, seen from multiple cameras.
+// The SmartProjectionPoseFactor only optimizes for the poses of a camera,
+// not the calibration, which is assumed known.
+#include <gtsam/slam/SmartProjectionPoseFactor.h>
+
+// For an explanation of these headers, see SFMExample.cpp
+#include "SFMdata.h"
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+
+using namespace std;
+using namespace gtsam;
+
+// Make the typename short so it looks much cleaner
+typedef SmartProjectionPoseFactor<Cal3_S2> SmartFactor;
+
+// create a typedef to the camera type
+typedef PinholePose<Cal3_S2> Camera;
+
+/* ************************************************************************* */
+int main(int argc, char* argv[]) {
+
+  // Define the camera calibration parameters
+  Cal3_S2::shared_ptr K(new Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
+
+  // Define the camera observation noise model
+  noiseModel::Isotropic::shared_ptr measurementNoise =
+      noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
+
+  // Create the set of ground-truth landmarks and poses
+  vector<Point3> points = createPoints();
+  vector<Pose3> poses = createPoses();
+
+  // Create a factor graph
+  NonlinearFactorGraph graph;
+
+  // Simulated measurements from each camera pose, adding them to the factor graph
+  for (size_t j = 0; j < points.size(); ++j) {
+
+    // every landmark represent a single landmark, we use shared pointer to init the factor, and then insert measurements.
+    SmartFactor::shared_ptr smartfactor(new SmartFactor(measurementNoise, K));
+
+    // for each measurement of landmark j
+    for (size_t i = 0; i < poses.size(); ++i) {
+
+      // generate the 2D measurement
+      Camera camera(poses[i], K);
+      Point2 measurement = camera.project(points[j]);
+
+      // call add() function to add measurement into a single factor, here we need to add:
+      //    1. the 2D measurement
+      //    2. the corresponding camera's key
+      //    3. camera noise model
+      //    4. camera calibration
+      smartfactor->add(measurement, i);
+    }
+
+    // insert the smart factor in the graph
+    graph.push_back(smartfactor);
+  }
+
+  // Add a prior on pose x0. This indirectly specifies where the origin is.
+  // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
+  noiseModel::Diagonal::shared_ptr noise = noiseModel::Diagonal::Sigmas(
+      (Vector(6) << Vector3::Constant(0.3)).finished());
+  graph.push_back(PriorFactor<Pose3>(0, poses[0], noise));
+
+  // Because the structure-from-motion problem has a scale ambiguity, the problem is
+  // still under-constrained. Here we add a prior on the second pose x1, so this will
+  // fix the scale by indicating the distance between x0 and x1.
+  // Because these two are fixed, the rest of the poses will be also be fixed.
+  graph.push_back(PriorFactor<Pose3>(1, poses[1], noise)); // add directly to graph
+
+  graph.print("Factor Graph:\n");
+
+  // Create the initial estimate to the solution
+  // Intentionally initialize the variables off from the ground truth
+  Values initialEstimate;
+  Pose3 delta(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
+  for (size_t i = 0; i < poses.size(); ++i)
+    initialEstimate.insert(i, poses[i].compose(delta));
+  initialEstimate.print("Initial Estimates:\n");
+
+  // Optimize the graph and print results
+  LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
+  Values result = optimizer.optimize();
+  result.print("Final results:\n");
+
+  // A smart factor represent the 3D point inside the factor, not as a variable.
+  // The 3D position of the landmark is not explicitly calculated by the optimizer.
+  // To obtain the landmark's 3D position, we use the "point" method of the smart factor.
+  Values landmark_result;
+  for (size_t j = 0; j < points.size(); ++j) {
+
+    // The graph stores Factor shared_ptrs, so we cast back to a SmartFactor first
+    SmartFactor::shared_ptr smart = boost::dynamic_pointer_cast<SmartFactor>(graph[j]);
+    if (smart) {
+      // The output of point() is in boost::optional<Point3>, as sometimes
+      // the triangulation operation inside smart factor will encounter degeneracy.
+      boost::optional<Point3> point = smart->point(result);
+      if (point) // ignore if boost::optional return NULL
+        landmark_result.insert(j, *point);
+    }
+  }
+
+  landmark_result.print("Landmark results:\n");
+  cout << "final error: " << graph.error(result) << endl;
+  cout << "number of iterations: " << optimizer.iterations() << endl;
+
+  return 0;
+}
+/* ************************************************************************* */
+