diff --git a/environment_linux_cpuonly.yml b/environment_linux_cpuonly.yml
index 415c51da9..fd046d2a9 100644
--- a/environment_linux_cpuonly.yml
+++ b/environment_linux_cpuonly.yml
@@ -23,7 +23,7 @@ dependencies:
   - asyncssh
   - python-graphviz
   # core functionality and APIs
-  - matplotlib==3.4.2
+  - matplotlib
   - networkx
   - numpy
   - nodejs
@@ -38,6 +38,7 @@ dependencies:
   - pytorch>=1.12.0
   - torchvision>=0.13.0
   - kornia
+  - pyceres
   - pycolmap
   # io
   - h5py
diff --git a/environment_v2_linux_cpuonly.yml b/environment_v2_linux_cpuonly.yml
new file mode 100644
index 000000000..2e1e18020
--- /dev/null
+++ b/environment_v2_linux_cpuonly.yml
@@ -0,0 +1,56 @@
+name: gtsfm-v2
+channels:
+  # for priority order, we prefer pytorch as the highest priority as it supplies
+  # latest stable packages for numerous deep learning based methods. conda-forge
+  # supplies higher versions of packages like opencv compared to the defaults
+  # channel.
+  - pytorch
+  - conda-forge
+dependencies:
+  # python essentials
+  - python
+  - pip
+  # formatting and dev environment
+  - black
+  - coverage
+  - mypy
+  - pylint
+  - pytest
+  - flake8
+  - isort
+  # dask and related
+  - dask # same as dask[complete] pip distribution
+  - asyncssh
+  - python-graphviz
+  # core functionality and APIs
+  - matplotlib
+  - networkx
+  - numpy
+  - nodejs
+  - pandas
+  - pillow
+  - scikit-learn
+  - seaborn
+  - scipy
+  - hydra-core
+  - gtsam
+  # 3rd party algorithms for different modules
+  - cpuonly # replacement of cudatoolkit for cpu only machines
+  - pytorch
+  - torchvision
+  - kornia
+  - pycolmap
+  - opencv
+  # io
+  - h5py
+  - plotly
+  - tabulate
+  - simplejson
+  - open3d
+  - colour
+  - pydot
+  - trimesh
+  # testing
+  - parameterized
+  # - pip:
+      # - pydegensac
diff --git a/gtsfm/averaging/rotation/gamma_values.py b/gtsfm/averaging/rotation/gamma_values.py
new file mode 100644
index 000000000..109cbfefb
--- /dev/null
+++ b/gtsfm/averaging/rotation/gamma_values.py
@@ -0,0 +1,12297 @@
+"""
+Constants for the Gamma distribution used in the MagSac loss.
+
+These values are copied over from the `GlobalSfmPy`'s source code:
+- https://raw.githubusercontent.com/zhangganlin/GlobalSfMpy/1c77e81a0b97884c81b8fc42c89e13049d9d4ad4/include/gamma_values.cpp
+"""
+
+NU3 = 3
+SIGMA_QUANTILE3 = 3.368214175218727
+C3 = 4.029720004054876e-01
+UPPER_INCOMPLETE_GAMMA_OF_K3 = 3.439485560754856e-03
+PRECISION_OF_STORED_GAMMA3 = 1000
+STORED_GAMMA_NUMBER3 = 36843
+
+STORED_GAMMA_VALUES3: list = [
+    1.00000000000000000e+00, 9.99000499833375133e-01, 9.98001998667332968e-01,
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+]
diff --git a/gtsfm/averaging/rotation/uncertainty_aware_rotation_averaging.py b/gtsfm/averaging/rotation/uncertainty_aware_rotation_averaging.py
new file mode 100644
index 000000000..e11b10a30
--- /dev/null
+++ b/gtsfm/averaging/rotation/uncertainty_aware_rotation_averaging.py
@@ -0,0 +1,251 @@
+"""
+Uncertainty aware rotation averaging.
+
+This algorithm was proposed in "Revisiting Rotation Averaging: Uncertainties and Robust Losses" and is implemented by 
+following the authors' source code (GNU GPL v3.0 license)
+
+References:
+- https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Revisiting_Rotation_Averaging_Uncertainties_and_Robust_Losses_CVPR_2023_paper.pdf
+- https://github.com/zhangganlin/GlobalSfMpy
+"""
+
+import math
+from enum import Enum
+from typing import Dict, List, NamedTuple, Optional, Tuple
+
+import numpy as np
+import pyceres
+from gtsam import Rot3
+import pycolmap
+import networkx as nx
+from gtsfm.common.pose_prior import PosePrior
+
+import gtsfm.utils.logger as logger_utils
+import gtsfm.averaging.rotation.gamma_values as rotation_const
+from gtsfm.averaging.rotation.rotation_averaging_base import RotationAveragingBase
+
+logger = logger_utils.get_logger()
+
+DUMMY_COVARIANCE = np.eye(3) * 1e-2
+
+
+class RotationInfo(NamedTuple):
+    i2Ri1: Rot3
+    covariance_mat: np.ndarray
+
+
+class RotationErrorType(Enum):
+    QUATERNION_NORM = 0
+    ROTATION_MAT_FNORM = 1
+    QUATERNION_COSINE = 2
+    ANGLE_AXIS_COVARIANCE = 3
+    ANGLE_AXIS = 4
+    ANGLE_AXIS_INLIERS = 5
+    ANGLE_AXIS_COV_INLIERS = 6
+    ANGLE_AXIS_COVTRACE = 7
+    ANGLE_AXIS_COVNORM = 8
+
+
+def random_rotation() -> Rot3:
+    """Sample a random rotation by generating a sample from the 4d unit sphere."""
+    q = np.random.randn(4) * 0.03
+    # Make unit-norm quaternion.
+    q /= np.linalg.norm(q)
+    qw, qx, qy, qz = q
+    R = Rot3(qw, qx, qy, qz)
+    return R
+
+
+def initialize_global_rotations_using_mst(num_images: int, i2Ri1_dict: Dict[Tuple[int, int], Rot3]) -> List[Rot3]:
+    # Create a graph from the relative rotations dictionary
+    graph = nx.Graph()
+    for i1, i2 in i2Ri1_dict.keys():
+        # TODO: use inlier count as weight
+        graph.add_edge(i1, i2, weight=1)
+
+    # Compute the Minimum Spanning Tree (MST)
+    mst = nx.minimum_spanning_tree(graph)
+
+    wRis = [random_rotation() for _ in range(num_images)]
+    for i1, i2 in sorted(mst.edges):
+        if (i1, i2) in i2Ri1_dict:
+            wRis[i2] = wRis[i1] * i2Ri1_dict[(i1, i2)].inverse()
+        else:
+            wRis[i2] = wRis[i1] * i2Ri1_dict[(i2, i1)]
+
+    return wRis
+
+
+class UncertaintyAwareRotationAveraging(RotationAveragingBase):
+    def run_rotation_averaging(
+        self,
+        num_images: int,
+        i2Ri1_dict: Dict[Tuple[int, int], Optional[Rot3]],
+        i1Ti2_priors: Dict[Tuple[int, int], PosePrior],
+        v_corr_idxs: Dict[Tuple[int, int], np.ndarray],
+    ) -> List[Optional[Rot3]]:
+        rotation_info_dict: Dict[Tuple[int, int], RotationInfo] = {
+            k: RotationInfo(i2Ri1=v, covariance_mat=DUMMY_COVARIANCE) for k, v in i2Ri1_dict.items() if v is not None
+        }
+
+        global_rotations_init = initialize_global_rotations_using_mst(
+            num_images, {key: value for key, value in i2Ri1_dict.items() if value is not None}
+        )
+
+        return _estimate_rotations_with_customized_loss_and_covariance_ceres(global_rotations_init, rotation_info_dict)
+
+
+class MAGSACWeightBasedLoss(pyceres.LossFunction):
+    """MAGSAC++ inspired loss proposed in "Revisiting Rotation Averaging: Uncertainties and Robust Losses"."""
+
+    def __init__(self, sigma: float, inverse: bool = False) -> None:
+        super().__init__()
+        self.sigma_max = sigma
+        self.nu = rotation_const.NU3
+        self.squared_sigma = self.sigma_max * self.sigma_max
+        self.squared_sigma_max_2 = 2.0 * self.squared_sigma
+        self.cubed_sigma_max = self.squared_sigma * self.sigma_max
+        self.dof_minus_one_per_two = (self.nu - 1.0) / 2.0
+        self.C_times_two_ad_dof = rotation_const.C3 * (2**self.dof_minus_one_per_two)
+        self.one_over_sigma = self.C_times_two_ad_dof / self.sigma_max
+        self.gamma_value = math.gamma(self.dof_minus_one_per_two)
+        self.gamma_difference = self.gamma_value - rotation_const.UPPER_INCOMPLETE_GAMMA_OF_K3
+        self.weight_zero = self.one_over_sigma * self.gamma_difference
+        self.use_weight_inverse = inverse
+
+    def Evaluate(self, squared_residual, rho):
+        zero_derivative = False
+        if squared_residual > rotation_const.SIGMA_QUANTILE3 * rotation_const.SIGMA_QUANTILE3 * self.squared_sigma:
+            squared_residual = rotation_const.SIGMA_QUANTILE3 * rotation_const.SIGMA_QUANTILE3 * self.squared_sigma
+            zero_derivative = True
+
+        x = round(rotation_const.PRECISION_OF_STORED_GAMMA3 * squared_residual / self.squared_sigma_max_2)
+        if rotation_const.STORED_GAMMA_NUMBER3 < x:
+            x = rotation_const.STORED_GAMMA_NUMBER3
+        s = x * self.squared_sigma_max_2 / rotation_const.PRECISION_OF_STORED_GAMMA3
+
+        weight = self.one_over_sigma * (
+            rotation_const.STORED_GAMMA_VALUES3[x] - rotation_const.UPPER_INCOMPLETE_GAMMA_OF_K3
+        )
+        weight_derivative = (
+            -self.C_times_two_ad_dof
+            * ((s / self.squared_sigma_max_2) ** (self.nu / 2 - 1.5))
+            * math.exp(-s / self.squared_sigma_max_2)
+            / (2 * self.cubed_sigma_max)
+        )
+        if s < 1e-7:
+            s = 1e-7
+        weight_second_derivative = (
+            2.0
+            * self.C_times_two_ad_dof
+            * ((s / self.squared_sigma_max_2) ** (self.nu / 2 - 1.5))
+            * (1.0 / self.squared_sigma - (self.nu - 3) / s)
+            * math.exp(-s / self.squared_sigma_max_2)
+            / (8 * self.cubed_sigma_max)
+        )
+
+        if self.use_weight_inverse:
+            rho[0] = 1.0 / weight
+            rho[1] = -1.0 / (weight * weight) * weight_derivative
+            rho[2] = 2.0 / (
+                weight * weight * weight
+            ) * weight_derivative * weight_derivative - weight_second_derivative / (weight * weight)
+            if zero_derivative:
+                rho[1] = 0.00001
+                rho[2] = 0.0
+        else:
+            rho[0] = self.weight_zero - weight
+            rho[1] = -weight_derivative
+            rho[2] = -weight_second_derivative
+            if rho[1] == 0:
+                rho[1] = 0.00001
+            if zero_derivative:
+                rho[1] = 0.00001
+                rho[2] = 0.0
+
+        return rho
+
+
+def _to_pycolmap_rigid3d(rotation: Rot3) -> pycolmap.Rigid3d:
+    gtsam_quat = rotation.toQuaternion()
+    rot = pycolmap.Rotation3d(np.array([gtsam_quat.x(), gtsam_quat.y(), gtsam_quat.z(), gtsam_quat.w()]))
+    return pycolmap.Rigid3d(rot, np.zeros(3))
+
+
+def _to_gtsam_rot(pycolmap_quat: np.ndarray) -> Rot3:
+    x, y, z, w = pycolmap_quat
+    return Rot3(w, x, y, z)
+
+
+def _estimate_rotations_with_customized_loss_and_covariance_ceres(
+    global_orientations_init: List[Rot3],
+    two_view_rotations: Dict[Tuple[int, int], RotationInfo],
+    rotation_error_type: RotationErrorType = RotationErrorType.ANGLE_AXIS_COVARIANCE,
+    loss: pyceres.LossFunction = pyceres.SoftLOneLoss(0.1),
+) -> List[Optional[Rot3]]:
+    if len(two_view_rotations) == 0:
+        logger.warning("Skipping nonlinear rotation optimization because no relative rotations were provided.")
+
+    problem = pyceres.Problem()
+
+    if rotation_error_type != RotationErrorType.ANGLE_AXIS_COVARIANCE:
+        raise NotImplementedError(f"Rotation error type {rotation_error_type} not implemented")
+
+    # Note(Ayush): wRi init
+    optimization_result = [_to_pycolmap_rigid3d(wRi) for wRi in global_orientations_init]
+
+    valid_rotations = set()
+
+    for view_id_pair, rotation_info in two_view_rotations.items():
+        i1, i2 = view_id_pair
+        valid_rotations.add(i1)
+        valid_rotations.add(i2)
+        rotation1 = global_orientations_init[i1]
+        rotation2 = global_orientations_init[i2]
+        covariance = rotation_info.covariance_mat
+
+        # Do not add the relative rotation constraint if it requires an orientation
+        # that we do not have an initialization for.
+        if rotation1 is None or rotation2 is None:
+            continue
+
+        costs = []
+        if rotation_error_type == RotationErrorType.ANGLE_AXIS_COVARIANCE:
+            pose_cov = np.eye(6)
+            pose_cov[:3, :3] = covariance
+            cost = pycolmap.cost_functions.MetricRelativePoseErrorCost(
+                _to_pycolmap_rigid3d(rotation_info.i2Ri1), np.zeros(3), pose_cov
+            )
+            costs.append(cost)
+            problem.add_residual_block(
+                cost,
+                loss,
+                [
+                    optimization_result[i1].rotation.quat,
+                    optimization_result[i1].translation,
+                    optimization_result[i2].rotation.quat,
+                    optimization_result[i2].translation,
+                ],
+            )
+
+        else:
+            logger.warning("Rotation error type %s skipped", rotation_error_type)
+
+    valid_rotations_list = list(valid_rotations)
+
+    problem.set_parameter_block_constant(optimization_result[valid_rotations_list[0]].rotation.quat)
+    problem.set_parameter_block_constant(optimization_result[valid_rotations_list[0]].translation)
+    for i in valid_rotations_list:
+        problem.set_manifold(optimization_result[i].rotation.quat, pyceres.QuaternionManifold())
+
+    options = pyceres.SolverOptions()
+    options.linear_solver_type = pyceres.LinearSolverType.SPARSE_NORMAL_CHOLESKY
+    options.minimizer_progress_to_stdout = True
+    options.num_threads = -1
+    summary = pyceres.SolverSummary()
+    pyceres.solve(options, problem, summary)
+    logger.info(summary.BriefReport())
+
+    wRis_list = [_to_gtsam_rot(rigid3d.rotation.quat) for rigid3d in optimization_result]
+
+    return [wRi if i in valid_rotations else None for i, wRi in enumerate(wRis_list)]
diff --git a/gtsfm/configs/unified.yaml b/gtsfm/configs/unified.yaml
index 493af70cd..5398cf376 100644
--- a/gtsfm/configs/unified.yaml
+++ b/gtsfm/configs/unified.yaml
@@ -68,8 +68,7 @@ SceneOptimizer:
       edge_error_aggregation_criterion: MIN_EDGE_ERROR
 
     rot_avg_module:
-      _target_: gtsfm.averaging.rotation.shonan.ShonanRotationAveraging
-      weight_by_inliers: True
+      _target_: gtsfm.averaging.rotation.uncertainty_aware_rotation_averaging.UncertaintyAwareRotationAveraging
 
     trans_avg_module:
       _target_: gtsfm.averaging.translation.averaging_1dsfm.TranslationAveraging1DSFM
diff --git a/tests/averaging/rotation/test_uncertainity_aware.py b/tests/averaging/rotation/test_uncertainity_aware.py
new file mode 100644
index 000000000..f6d25cb35
--- /dev/null
+++ b/tests/averaging/rotation/test_uncertainity_aware.py
@@ -0,0 +1,131 @@
+"""Tests for Shonan rotation averaging.
+
+Authors: Ayush Baid, John Lambert
+"""
+
+import pickle
+import unittest
+from typing import Dict, List, Tuple
+
+import dask
+import numpy as np
+from gtsam import Pose3, Rot3
+
+import gtsfm.utils.geometry_comparisons as geometry_comparisons
+import tests.data.sample_poses as sample_poses
+from gtsfm.averaging.rotation.rotation_averaging_base import RotationAveragingBase
+from gtsfm.averaging.rotation.uncertainty_aware_rotation_averaging import UncertaintyAwareRotationAveraging
+from gtsfm.common.pose_prior import PosePrior, PosePriorType
+
+ROTATION_ANGLE_ERROR_THRESHOLD_DEG = 2
+
+
+class TestUncertainityAwareRotationAveraging(unittest.TestCase):
+    """Test class for Shonan rotation averaging.
+
+    All unit test functions defined in TestRotationAveragingBase are run automatically.
+    """
+
+    def setUp(self):
+        super().setUp()
+
+        self.obj: RotationAveragingBase = UncertaintyAwareRotationAveraging()
+
+    def __execute_test(self, i2Ri1_input: Dict[Tuple[int, int], Rot3], wRi_expected: List[Rot3]) -> None:
+        """Helper function to run the averagaing and assert w/ expected.
+
+        Args:
+            i2Ri1_input: relative rotations, which are input to the algorithm.
+            wRi_expected: expected global rotations.
+        """
+        i1Ti2_priors: Dict[Tuple[int, int], PosePrior] = {}
+        wRi_computed = self.obj.run_rotation_averaging(len(wRi_expected), i2Ri1_input, i1Ti2_priors)
+        self.assertTrue(
+            geometry_comparisons.compare_rotations(wRi_computed, wRi_expected, ROTATION_ANGLE_ERROR_THRESHOLD_DEG)
+        )
+
+    def test_circle_two_edges(self):
+        """Test for 4 poses in a circle, with a pose connected to its immediate neighborhood."""
+        i2Ri1_dict, wRi_expected = sample_poses.convert_data_for_rotation_averaging(
+            sample_poses.CIRCLE_TWO_EDGES_GLOBAL_POSES, sample_poses.CIRCLE_TWO_EDGES_RELATIVE_POSES
+        )
+        self.__execute_test(i2Ri1_dict, wRi_expected)
+
+    def test_circle_all_edges(self):
+        """Test for 4 poses in a circle, with a pose connected all others."""
+        i2Ri1_dict, wRi_expected = sample_poses.convert_data_for_rotation_averaging(
+            sample_poses.CIRCLE_ALL_EDGES_GLOBAL_POSES, sample_poses.CIRCLE_ALL_EDGES_RELATIVE_POSES
+        )
+        self.__execute_test(i2Ri1_dict, wRi_expected)
+
+    def test_line_large_edges(self):
+        """Test for 3 poses in a line, with large translations between them."""
+        i2Ri1_dict, wRi_expected = sample_poses.convert_data_for_rotation_averaging(
+            sample_poses.LINE_LARGE_EDGES_GLOBAL_POSES, sample_poses.LINE_LARGE_EDGES_RELATIVE_POSES
+        )
+        self.__execute_test(i2Ri1_dict, wRi_expected)
+
+    def test_panorama(self):
+        """Test for 3 poses in a panorama configuration (large rotations at the same location)"""
+        i2Ri1_dict, wRi_expected = sample_poses.convert_data_for_rotation_averaging(
+            sample_poses.PANORAMA_GLOBAL_POSES, sample_poses.PANORAMA_RELATIVE_POSES
+        )
+        self.__execute_test(i2Ri1_dict, wRi_expected)
+
+    def test_simple(self):
+        """Test a simple case with three relative rotations."""
+
+        i2Ri1_dict = {
+            (1, 0): Rot3.RzRyRx(0, np.deg2rad(30), 0),
+            (2, 1): Rot3.RzRyRx(0, 0, np.deg2rad(20)),
+        }
+
+        expected_wRi_list = [
+            Rot3.RzRyRx(0, 0, 0),
+            Rot3.RzRyRx(0, np.deg2rad(30), 0),
+            i2Ri1_dict[(1, 0)].compose(i2Ri1_dict[(2, 1)]),
+        ]
+
+        self.__execute_test(i2Ri1_dict, expected_wRi_list)
+
+    #     )
+
+    def test_computation_graph(self):
+        """Test the dask computation graph execution using a valid collection of relative poses."""
+
+        num_poses = 3
+
+        i2Ri1_dict = {
+            (0, 1): Rot3.RzRyRx(0, np.deg2rad(30), 0),
+            (1, 2): Rot3.RzRyRx(0, 0, np.deg2rad(20)),
+        }
+
+        i2Ri1_graph = dask.delayed(i2Ri1_dict)
+
+        # use the GTSAM API directly (without dask) for rotation averaging
+        i1Ti2_priors: Dict[Tuple[int, int], PosePrior] = {}
+        expected_wRi_list = self.obj.run_rotation_averaging(num_poses, i2Ri1_dict, i1Ti2_priors)
+
+        # use dask's computation graph
+        gt_wTi_list = [None] * len(expected_wRi_list)
+        rotations_graph, _ = self.obj.create_computation_graph(num_poses, i2Ri1_graph, i1Ti2_priors, gt_wTi_list)
+
+        with dask.config.set(scheduler="single-threaded"):
+            wRi_list = dask.compute(rotations_graph)[0]
+
+        # compare the two results
+        self.assertTrue(
+            geometry_comparisons.compare_rotations(wRi_list, expected_wRi_list, ROTATION_ANGLE_ERROR_THRESHOLD_DEG)
+        )
+
+    def test_pickleable(self):
+        """Tests that the object is pickleable (required for dask)."""
+
+        try:
+            pickle.dumps(self.obj)
+        except TypeError:
+            self.fail("Cannot dump rotation averaging object using pickle")
+
+
+if __name__ == "__main__":
+    unittest.main()