diff --git a/gtsfm/averaging/rotation/shonan.py b/gtsfm/averaging/rotation/shonan.py
index ad8643a43..e9108e80b 100644
--- a/gtsfm/averaging/rotation/shonan.py
+++ b/gtsfm/averaging/rotation/shonan.py
@@ -20,6 +20,8 @@
     Rot3,
     ShonanAveraging3,
     ShonanAveragingParameters3,
+    BetweenFactorPose3,
+    Pose3,
 )
 
 import gtsfm.utils.logger as logger_utils
@@ -38,7 +40,10 @@ class ShonanRotationAveraging(RotationAveragingBase):
     """Performs Shonan rotation averaging."""
 
     def __init__(
-        self, two_view_rotation_sigma: float = _DEFAULT_TWO_VIEW_ROTATION_SIGMA, weight_by_inliers: bool = True
+        self,
+        two_view_rotation_sigma: float = _DEFAULT_TWO_VIEW_ROTATION_SIGMA,
+        weight_by_inliers: bool = True,
+        use_chordal_init: bool = True,
     ) -> None:
         """Initializes module.
 
@@ -54,12 +59,19 @@ def __init__(
         self._p_min = 3
         self._p_max = 64
         self._weight_by_inliers = weight_by_inliers
+        self._use_chordal_init = use_chordal_init
 
     def __get_shonan_params(self) -> ShonanAveragingParameters3:
         lm_params = LevenbergMarquardtParams.CeresDefaults()
+        # TODO(akshay-krishnan): These parameters speed up Shonan, but disabled now because accuracy dropped slightly.
+        # lm_params.setRelativeErrorTol(0.01)
+        # lm_params.setAbsoluteErrorTol(1)
         shonan_params = ShonanAveragingParameters3(lm_params)
         shonan_params.setUseHuber(False)
         shonan_params.setCertifyOptimality(True)
+        shonan_params.setGaugesWeight(0.0)
+        shonan_params.setKarcherWeight(1.0)
+        shonan_params.setAnchorWeight(0.0)
         return shonan_params
 
     def __measurements_from_2view_relative_rotations(
@@ -132,9 +144,18 @@ def _run_with_consecutive_ordering(
             len(measurements),
             num_connected_nodes,
         )
-        shonan = ShonanAveraging3(measurements, self.__get_shonan_params())
+        shonan_params = self.__get_shonan_params()
+        if self._use_chordal_init:
+            shonan_params.setKarcherWeight(0.0)
+            shonan_params.setAnchorWeight(1.0)
+            shonan_params.setAnchor(measurements[0].key1(), Rot3())
+        shonan = ShonanAveraging3(measurements, shonan_params)
+
+        if self._use_chordal_init:
+            initial = self.chordal_initialize(measurements)
+        else:
+            initial = shonan.initializeRandomly()
 
-        initial = shonan.initializeRandomly()
         logger.info("Initial cost: %.5f", shonan.cost(initial))
         result, _ = shonan.run(initial, self._p_min, self._p_max)
         logger.info("Final cost: %.5f", shonan.cost(result))
@@ -161,6 +182,27 @@ def _nodes_with_edges(
 
         return unique_nodes_with_edges
 
+    def chordal_initialize(self, measurements: gtsam.BinaryMeasurementsRot3) -> gtsam.Values:
+        """Initialize values using GTSAM's chordal init.
+
+        Args:
+            measurements: BinaryMeasurementsRot3 object created before running Shonan.
+
+        Returns:
+            Initial values as a gtsam.Values object.
+        """
+        graph = gtsam.NonlinearFactorGraph()
+        anchor_key = None
+        noise_model = gtsam.noiseModel.Diagonal.Variances(np.array([1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4], dtype=float))
+        for measurement in measurements:
+            if anchor_key is None:
+                anchor_key = measurement.key1()
+            pose_measurement = Pose3(measurement.measured(), np.array([0.0, 0.0, 0.0]))
+            graph.add(BetweenFactorPose3(measurement.key1(), measurement.key2(), pose_measurement, noise_model))
+
+        graph.addPriorPose3(anchor_key, Pose3(), noise_model)
+        return gtsam.InitializePose3.initializeOrientations(graph)
+
     def run_rotation_averaging(
         self,
         num_images: int,