diff --git a/examples/SFMExample_SmartProjectionPoseFactor.cpp b/examples/SFMExample_SmartProjectionPoseFactor.cpp
deleted file mode 100644
index 794bd3901..000000000
--- a/examples/SFMExample_SmartProjectionPoseFactor.cpp
+++ /dev/null
@@ -1,132 +0,0 @@
-/* ----------------------------------------------------------------------------
-
- * 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;
-}
-/* ************************************************************************* */
-