diff --git a/gtsam/sfm/TranslationRecovery.cpp b/gtsam/sfm/TranslationRecovery.cpp
index 665336cc4..319129840 100644
--- a/gtsam/sfm/TranslationRecovery.cpp
+++ b/gtsam/sfm/TranslationRecovery.cpp
@@ -45,9 +45,8 @@ NonlinearFactorGraph TranslationRecovery::buildGraph() const {
 }
 
 void TranslationRecovery::addPrior(const double scale,
-                                   NonlinearFactorGraph *graph) const {
-  //TODO(akshay-krishnan): make this an input argument                                     
-  auto priorNoiseModel = noiseModel::Isotropic::Sigma(3, 0.01);
+                                   NonlinearFactorGraph *graph,
+                                   const SharedNoiseModel &priorNoiseModel) const {
   auto edge = relativeTranslations_.begin();
   graph->emplace_shared<PriorFactor<Point3> >(edge->key1(), Point3(0, 0, 0), priorNoiseModel);
   graph->emplace_shared<PriorFactor<Point3> >(edge->key2(), scale * edge->measured().point3(),
diff --git a/gtsam/sfm/TranslationRecovery.h b/gtsam/sfm/TranslationRecovery.h
index 4c6a95cbe..d5538f91b 100644
--- a/gtsam/sfm/TranslationRecovery.h
+++ b/gtsam/sfm/TranslationRecovery.h
@@ -68,9 +68,7 @@ class TranslationRecovery {
    */
   TranslationRecovery(const TranslationEdges &relativeTranslations,
                       const LevenbergMarquardtParams &lmParams = LevenbergMarquardtParams())
-      : relativeTranslations_(relativeTranslations), params_(lmParams) {
-    params_.setVerbosityLM("Summary");
-  }
+      : relativeTranslations_(relativeTranslations), params_(lmParams) {}
 
   /**
    * @brief Build the factor graph to do the optimization.
@@ -84,8 +82,11 @@ class TranslationRecovery {
    *
    * @param scale scale for first relative translation which fixes gauge.
    * @param graph factor graph to which prior is added.
+   * @param priorNoiseModel the noise model to use with the prior.
    */
-  void addPrior(const double scale, NonlinearFactorGraph *graph) const;
+  void addPrior(const double scale, NonlinearFactorGraph *graph,
+                const SharedNoiseModel &priorNoiseModel =
+                    noiseModel::Isotropic::Sigma(3, 0.01)) const;
 
   /**
    * @brief Create random initial translations.
diff --git a/python/gtsam/tests/test_TranslationRecovery.py b/python/gtsam/tests/test_TranslationRecovery.py
index ec6d6a83f..0fb0518b6 100644
--- a/python/gtsam/tests/test_TranslationRecovery.py
+++ b/python/gtsam/tests/test_TranslationRecovery.py
@@ -40,7 +40,12 @@ class TestTranslationRecovery(unittest.TestCase):
     def test_run(self):
         gt_poses = ExampleValues()
         measurements = SimulateMeasurements(gt_poses, [[0, 1], [0, 2], [1, 2]])
-        algorithm = gtsam.TranslationRecovery(measurements)
+
+        # Set verbosity to Silent for tests
+        lmParams = gtsam.LevenbergMarquardtParams()
+        lmParams.setVerbosityLM("silent")
+
+        algorithm = gtsam.TranslationRecovery(measurements, lmParams)
         scale = 2.0
         result = algorithm.run(scale)