diff --git a/examples/SFMExample_SmartFactor_bal.cpp b/examples/SFMExample_SmartFactor_bal.cpp
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+++ b/examples/SFMExample_SmartFactor_bal.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_bal.cpp
+ * @brief   Solve a BAL structure-from-motion problem using smart projection factors.
+ */
+
+#include <gtsam/sfm/SfmData.h>
+#include <gtsam/slam/SmartProjectionFactor.h>
+#include <gtsam/slam/dataset.h>
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+#include <gtsam/inference/Symbol.h>
+
+#include <vector>
+
+using namespace std;
+using namespace gtsam;
+using symbol_shorthand::C;
+
+// Smart factor on full SfmCamera state (pose + calibration).
+typedef SmartProjectionFactor<SfmCamera> SmartFactor;
+
+/* ************************************************************************* */
+int main(int argc, char* argv[]) {
+  string filename = findExampleDataFile("dubrovnik-3-7-pre");
+  if (argc > 1) filename = string(argv[1]);
+
+  SfmData mydata = SfmData::FromBalFile(filename);
+  cout << "read " << mydata.numberTracks() << " tracks on " << mydata.numberCameras() << " cameras" << endl;
+
+  if (mydata.numberCameras() < 2) {
+    cerr << "Need at least 2 cameras for this example." << endl;
+    return 1;
+  }
+
+  NonlinearFactorGraph graph;
+  auto measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0);
+
+  for (const SfmTrack& track : mydata.tracks) {
+    SmartFactor::shared_ptr smart(new SmartFactor(measurementNoise));
+    for (const auto& [i, uv] : track.measurements) {
+      smart->add(uv, C(i));
+    }
+    graph.push_back(smart);
+  }
+
+  // Fix gauge/scale with priors on two cameras.
+  graph.addPrior(C(0), mydata.cameras[0], noiseModel::Isotropic::Sigma(9, 0.1));
+  graph.addPrior(C(1), mydata.cameras[1], noiseModel::Isotropic::Sigma(9, 0.1));
+
+  Values initial;
+  size_t i = 0;
+  for (const SfmCamera& camera : mydata.cameras) initial.insert(C(i++), camera);
+
+  Values result;
+  size_t iterations = 0;
+  try {
+    LevenbergMarquardtParams params;
+    params.setVerbosity("ERROR");
+    LevenbergMarquardtOptimizer lm(graph, initial, params);
+    result = lm.optimize();
+    iterations = lm.iterations();
+  } catch (exception& e) {
+    cout << e.what() << endl;
+    return 1;
+  }
+
+  cout << "final error: " << graph.error(result) << endl;
+  cout << "iterations: " << iterations << endl;
+  return 0;
+}
+/* ************************************************************************* */
+