diff --git a/pytorch3d/transforms/__init__.py b/pytorch3d/transforms/__init__.py
index efa0d6310..81da49571 100644
--- a/pytorch3d/transforms/__init__.py
+++ b/pytorch3d/transforms/__init__.py
@@ -20,6 +20,7 @@
     rotation_6d_to_matrix,
     standardize_quaternion,
 )
+from .se3 import se3_exp_map, se3_log_map
 from .so3 import (
     so3_exponential_map,
     so3_exp_map,
diff --git a/pytorch3d/transforms/se3.py b/pytorch3d/transforms/se3.py
new file mode 100644
index 000000000..e14c720ad
--- /dev/null
+++ b/pytorch3d/transforms/se3.py
@@ -0,0 +1,213 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+import torch
+
+from .so3 import hat, _so3_exp_map, so3_log_map
+
+
+def se3_exp_map(log_transform: torch.Tensor, eps: float = 1e-4) -> torch.Tensor:
+    """
+    Convert a batch of logarithmic representations of SE(3) matrices `log_transform`
+    to a batch of 4x4 SE(3) matrices using the exponential map.
+    See e.g. [1], Sec 9.4.2. for more detailed description.
+
+    A SE(3) matrix has the following form:
+        ```
+        [ R 0 ]
+        [ T 1 ] ,
+        ```
+    where `R` is a 3x3 rotation matrix and `T` is a 3-D translation vector.
+    SE(3) matrices are commonly used to represent rigid motions or camera extrinsics.
+
+    In the SE(3) logarithmic representation SE(3) matrices are
+    represented as 6-dimensional vectors `[log_translation | log_rotation]`,
+    i.e. a concatenation of two 3D vectors `log_translation` and `log_rotation`.
+
+    The conversion from the 6D representation to a 4x4 SE(3) matrix `transform`
+    is done as follows:
+        ```
+        transform = exp( [ hat(log_rotation) 0 ]
+                         [   log_translation 1 ] ) ,
+        ```
+    where `exp` is the matrix exponential and `hat` is the Hat operator [2].
+
+    Note that for any `log_transform` with `0 <= ||log_rotation|| < 2pi`
+    (i.e. the rotation angle is between 0 and 2pi), the following identity holds:
+    ```
+    se3_log_map(se3_exponential_map(log_transform)) == log_transform
+    ```
+
+    The conversion has a singularity around `||log(transform)|| = 0`
+    which is handled by clamping controlled with the `eps` argument.
+
+    Args:
+        log_transform: Batch of vectors of shape `(minibatch, 6)`.
+        eps: A threshold for clipping the squared norm of the rotation logarithm
+            to avoid unstable gradients in the singular case.
+
+    Returns:
+        Batch of transformation matrices of shape `(minibatch, 4, 4)`.
+
+    Raises:
+        ValueError if `log_transform` is of incorrect shape.
+
+    [1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf
+    [2] https://en.wikipedia.org/wiki/Hat_operator
+    """
+
+    if log_transform.ndim != 2 or log_transform.shape[1] != 6:
+        raise ValueError("Expected input to be of shape (N, 6).")
+
+    N, _ = log_transform.shape
+
+    log_translation = log_transform[..., :3]
+    log_rotation = log_transform[..., 3:]
+
+    # rotation is an exponential map of log_rotation
+    (
+        R,
+        rotation_angles,
+        log_rotation_hat,
+        log_rotation_hat_square,
+    ) = _so3_exp_map(log_rotation, eps=eps)
+
+    # translation is V @ T
+    V = _se3_V_matrix(
+        log_rotation,
+        log_rotation_hat,
+        log_rotation_hat_square,
+        rotation_angles,
+        eps=eps,
+    )
+    T = torch.bmm(V, log_translation[:, :, None])[:, :, 0]
+
+    transform = torch.zeros(
+        N, 4, 4, dtype=log_transform.dtype, device=log_transform.device
+    )
+
+    transform[:, :3, :3] = R
+    transform[:, :3, 3] = T
+    transform[:, 3, 3] = 1.0
+
+    return transform.permute(0, 2, 1)
+
+
+def se3_log_map(
+    transform: torch.Tensor, eps: float = 1e-4, cos_bound: float = 1e-4
+) -> torch.Tensor:
+    """
+    Convert a batch of 4x4 transformation matrices `transform`
+    to a batch of 6-dimensional SE(3) logarithms of the SE(3) matrices.
+    See e.g. [1], Sec 9.4.2. for more detailed description.
+
+    A SE(3) matrix has the following form:
+        ```
+        [ R 0 ]
+        [ T 1 ] ,
+        ```
+    where `R` is an orthonormal 3x3 rotation matrix and `T` is a 3-D translation vector.
+    SE(3) matrices are commonly used to represent rigid motions or camera extrinsics.
+
+    In the SE(3) logarithmic representation SE(3) matrices are
+    represented as 6-dimensional vectors `[log_translation | log_rotation]`,
+    i.e. a concatenation of two 3D vectors `log_translation` and `log_rotation`.
+
+    The conversion from the 4x4 SE(3) matrix `transform` to the
+    6D representation `log_transform = [log_translation | log_rotation]`
+    is done as follows:
+        ```
+        log_transform = log(transform)
+        log_translation = log_transform[3, :3]
+        log_rotation = inv_hat(log_transform[:3, :3])
+        ```
+    where `log` is the matrix logarithm
+    and `inv_hat` is the inverse of the Hat operator [2].
+
+    Note that for any valid 4x4 `transform` matrix, the following identity holds:
+    ```
+    se3_exp_map(se3_log_map(transform)) == transform
+    ```
+
+    The conversion has a singularity around `(transform=I)` which is handled
+    by clamping controlled with the `eps` and `cos_bound` arguments.
+
+    Args:
+        transform: batch of SE(3) matrices of shape `(minibatch, 4, 4)`.
+        eps: A threshold for clipping the squared norm of the rotation logarithm
+            to avoid division by zero in the singular case.
+        cos_bound: Clamps the cosine of the rotation angle to
+            [-1 + cos_bound, 3 - cos_bound] to avoid non-finite outputs.
+            The non-finite outputs can be caused by passing small rotation angles
+            to the `acos` function in `so3_rotation_angle` of `so3_log_map`.
+
+    Returns:
+        Batch of logarithms of input SE(3) matrices
+        of shape `(minibatch, 6)`.
+
+    Raises:
+        ValueError if `transform` is of incorrect shape.
+        ValueError if `R` has an unexpected trace.
+
+    [1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf
+    [2] https://en.wikipedia.org/wiki/Hat_operator
+    """
+
+    if transform.ndim != 3:
+        raise ValueError("Input tensor shape has to be (N, 4, 4).")
+
+    N, dim1, dim2 = transform.shape
+    if dim1 != 4 or dim2 != 4:
+        raise ValueError("Input tensor shape has to be (N, 4, 4).")
+
+    if not torch.allclose(transform[:, :3, 3], torch.zeros_like(transform[:, :3, 3])):
+        raise ValueError("All elements of `transform[:, :3, 3]` should be 0.")
+
+    # log_rot is just so3_log_map of the upper left 3x3 block
+    R = transform[:, :3, :3].permute(0, 2, 1)
+    log_rotation = so3_log_map(R, eps=eps, cos_bound=cos_bound)
+
+    # log_translation is V^-1 @ T
+    T = transform[:, 3, :3]
+    V = _se3_V_matrix(*_get_se3_V_input(log_rotation), eps=eps)
+    log_translation = torch.linalg.solve(V, T[:, :, None])[:, :, 0]
+
+    return torch.cat((log_translation, log_rotation), dim=1)
+
+
+def _se3_V_matrix(
+    log_rotation: torch.Tensor,
+    log_rotation_hat: torch.Tensor,
+    log_rotation_hat_square: torch.Tensor,
+    rotation_angles: torch.Tensor,
+    eps: float = 1e-4,
+) -> torch.Tensor:
+    """
+    A helper function that computes the "V" matrix from [1], Sec 9.4.2.
+    [1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf
+    """
+
+    V = (
+        torch.eye(3, dtype=log_rotation.dtype, device=log_rotation.device)[None]
+        + log_rotation_hat
+        * ((1 - torch.cos(rotation_angles)) / (rotation_angles ** 2))[:, None, None]
+        + (
+            log_rotation_hat_square
+            * ((rotation_angles - torch.sin(rotation_angles)) / (rotation_angles ** 3))[
+                :, None, None
+            ]
+        )
+    )
+
+    return V
+
+
+def _get_se3_V_input(log_rotation: torch.Tensor, eps: float = 1e-4):
+    """
+    A helper function that computes the input variables to the `_se3_V_matrix`
+    function.
+    """
+    nrms = (log_rotation ** 2).sum(-1)
+    rotation_angles = torch.clamp(nrms, eps).sqrt()
+    log_rotation_hat = hat(log_rotation)
+    log_rotation_hat_square = torch.bmm(log_rotation_hat, log_rotation_hat)
+    return log_rotation, log_rotation_hat, log_rotation_hat_square, rotation_angles
diff --git a/pytorch3d/transforms/so3.py b/pytorch3d/transforms/so3.py
index 750a47b84..5de8ee8e8 100644
--- a/pytorch3d/transforms/so3.py
+++ b/pytorch3d/transforms/so3.py
@@ -14,6 +14,7 @@ def so3_relative_angle(
     R2: torch.Tensor,
     cos_angle: bool = False,
     cos_bound: float = 1e-4,
+    eps: float = 1e-4,
 ) -> torch.Tensor:
     """
     Calculates the relative angle (in radians) between pairs of
@@ -33,7 +34,8 @@ def so3_relative_angle(
             of the `acos` call. Note that the non-finite outputs/gradients
             are returned when the angle is requested (i.e. `cos_angle==False`)
             and the rotation angle is close to 0 or π.
-
+        eps: Tolerance for the valid trace check of the relative rotation matrix
+            in `so3_rotation_angle`.
     Returns:
         Corresponding rotation angles of shape `(minibatch,)`.
         If `cos_angle==True`, returns the cosine of the angles.
@@ -43,7 +45,7 @@ def so3_relative_angle(
         ValueError if `R1` or `R2` has an unexpected trace.
     """
     R12 = torch.bmm(R1, R2.permute(0, 2, 1))
-    return so3_rotation_angle(R12, cos_angle=cos_angle, cos_bound=cos_bound)
+    return so3_rotation_angle(R12, cos_angle=cos_angle, cos_bound=cos_bound, eps=eps)
 
 
 def so3_rotation_angle(
diff --git a/tests/bm_se3.py b/tests/bm_se3.py
new file mode 100644
index 000000000..d6e92e200
--- /dev/null
+++ b/tests/bm_se3.py
@@ -0,0 +1,19 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+from fvcore.common.benchmark import benchmark
+from test_se3 import TestSE3
+
+
+def bm_se3() -> None:
+    kwargs_list = [
+        {"batch_size": 1},
+        {"batch_size": 10},
+        {"batch_size": 100},
+        {"batch_size": 1000},
+    ]
+    benchmark(TestSE3.se3_expmap, "SE3_EXP", kwargs_list, warmup_iters=1)
+    benchmark(TestSE3.se3_logmap, "SE3_LOG", kwargs_list, warmup_iters=1)
+
+
+if __name__ == "__main__":
+    bm_se3()
diff --git a/tests/test_se3.py b/tests/test_se3.py
new file mode 100644
index 000000000..0338e9b81
--- /dev/null
+++ b/tests/test_se3.py
@@ -0,0 +1,324 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+
+import unittest
+
+import numpy as np
+import torch
+from common_testing import TestCaseMixin
+from pytorch3d.transforms.rotation_conversions import random_rotations
+from pytorch3d.transforms.se3 import se3_exp_map, se3_log_map
+from pytorch3d.transforms.so3 import (
+    so3_exp_map,
+    so3_log_map,
+    so3_rotation_angle,
+)
+
+
+class TestSE3(TestCaseMixin, unittest.TestCase):
+    precomputed_log_transform = torch.tensor(
+        [
+            [0.1900, 2.1600, -0.1700, 0.8500, -1.9200, 0.6500],
+            [-0.6500, -0.8200, 0.5300, -1.2800, -1.6600, -0.3000],
+            [-0.0900, 0.2000, -1.1200, 1.8600, -0.7100, 0.6900],
+            [0.8000, -0.0300, 1.4900, -0.5200, -0.2500, 1.4700],
+            [-0.3300, -1.1600, 2.3600, -0.6900, 0.1800, -1.1800],
+            [-1.8000, -1.5800, 0.8400, 1.4200, 0.6500, 0.4300],
+            [-1.5900, 0.6200, 1.6900, -0.6600, 0.9400, 0.0800],
+            [0.0800, -0.1400, 0.3300, -0.5900, -1.0700, 0.1000],
+            [-0.3300, -0.5300, -0.8800, 0.3900, 0.1600, -0.2000],
+            [1.0100, -1.3500, -0.3500, -0.6400, 0.4500, -0.5400],
+        ],
+        dtype=torch.float32,
+    )
+
+    precomputed_transform = torch.tensor(
+        [
+            [
+                [-0.3496, -0.2966, 0.8887, 0.0000],
+                [-0.7755, 0.6239, -0.0968, 0.0000],
+                [-0.5258, -0.7230, -0.4481, 0.0000],
+                [-0.7392, 1.9119, 0.3122, 1.0000],
+            ],
+            [
+                [0.0354, 0.5992, 0.7998, 0.0000],
+                [0.8413, 0.4141, -0.3475, 0.0000],
+                [-0.5395, 0.6852, -0.4894, 0.0000],
+                [-0.9902, -0.4840, 0.1226, 1.0000],
+            ],
+            [
+                [0.6664, -0.1679, 0.7264, 0.0000],
+                [-0.7309, -0.3394, 0.5921, 0.0000],
+                [0.1471, -0.9255, -0.3489, 0.0000],
+                [-0.0815, 0.8719, -0.4516, 1.0000],
+            ],
+            [
+                [0.1010, 0.9834, -0.1508, 0.0000],
+                [-0.8783, 0.0169, -0.4779, 0.0000],
+                [-0.4674, 0.1807, 0.8654, 0.0000],
+                [0.2375, 0.7043, 1.4159, 1.0000],
+            ],
+            [
+                [0.3935, -0.8930, 0.2184, 0.0000],
+                [0.7873, 0.2047, -0.5817, 0.0000],
+                [0.4747, 0.4009, 0.7836, 0.0000],
+                [-0.3476, -0.0424, 2.5408, 1.0000],
+            ],
+            [
+                [0.7572, 0.6342, -0.1567, 0.0000],
+                [0.1039, 0.1199, 0.9873, 0.0000],
+                [0.6449, -0.7638, 0.0249, 0.0000],
+                [-1.2885, -2.0666, -0.1137, 1.0000],
+            ],
+            [
+                [0.6020, -0.2140, -0.7693, 0.0000],
+                [-0.3409, 0.8024, -0.4899, 0.0000],
+                [0.7221, 0.5572, 0.4101, 0.0000],
+                [-0.7550, 1.1928, 1.8480, 1.0000],
+            ],
+            [
+                [0.4913, 0.3548, 0.7954, 0.0000],
+                [0.2013, 0.8423, -0.5000, 0.0000],
+                [-0.8474, 0.4058, 0.3424, 0.0000],
+                [-0.1003, -0.0406, 0.3295, 1.0000],
+            ],
+            [
+                [0.9678, -0.1622, -0.1926, 0.0000],
+                [0.2235, 0.9057, 0.3603, 0.0000],
+                [0.1160, -0.3917, 0.9128, 0.0000],
+                [-0.4417, -0.3111, -0.9227, 1.0000],
+            ],
+            [
+                [0.7710, -0.5957, -0.2250, 0.0000],
+                [0.3288, 0.6750, -0.6605, 0.0000],
+                [0.5454, 0.4352, 0.7163, 0.0000],
+                [0.5623, -1.5886, -0.0182, 1.0000],
+            ],
+        ],
+        dtype=torch.float32,
+    )
+
+    def setUp(self) -> None:
+        super().setUp()
+        torch.manual_seed(42)
+        np.random.seed(42)
+
+    @staticmethod
+    def init_log_transform(batch_size: int = 10):
+        """
+        Initialize a list of `batch_size` 6-dimensional vectors representing
+        randomly generated logarithms of SE(3) transforms.
+        """
+        device = torch.device("cuda:0")
+        log_rot = torch.randn((batch_size, 6), dtype=torch.float32, device=device)
+        return log_rot
+
+    @staticmethod
+    def init_transform(batch_size: int = 10):
+        """
+        Initialize a list of `batch_size` 4x4 SE(3) transforms.
+        """
+        device = torch.device("cuda:0")
+        transform = torch.zeros(batch_size, 4, 4, dtype=torch.float32, device=device)
+        transform[:, :3, :3] = random_rotations(
+            batch_size, dtype=torch.float32, device=device
+        )
+        transform[:, 3, :3] = torch.randn(
+            (batch_size, 3), dtype=torch.float32, device=device
+        )
+        transform[:, 3, 3] = 1.0
+        return transform
+
+    def test_se3_exp_output_format(self, batch_size: int = 100):
+        """
+        Check that the output of `se3_exp_map` is a valid SE3 matrix.
+        """
+        transform = se3_exp_map(TestSE3.init_log_transform(batch_size=batch_size))
+        R = transform[:, :3, :3]
+        T = transform[:, 3, :3]
+        rest = transform[:, :, 3]
+        Rdet = R.det()
+
+        # check det(R)==1
+        self.assertClose(Rdet, torch.ones_like(Rdet), atol=1e-4)
+
+        # check that the translation is a finite vector
+        self.assertTrue(torch.isfinite(T).all())
+
+        # check last column == [0,0,0,1]
+        last_col = rest.new_zeros(batch_size, 4)
+        last_col[:, -1] = 1.0
+        self.assertClose(rest, last_col)
+
+    def test_compare_with_precomputed(self):
+        """
+        Compare the outputs against precomputed results.
+        """
+        self.assertClose(
+            se3_log_map(self.precomputed_transform),
+            self.precomputed_log_transform,
+            atol=1e-4,
+        )
+        self.assertClose(
+            self.precomputed_transform,
+            se3_exp_map(self.precomputed_log_transform),
+            atol=1e-4,
+        )
+
+    def test_se3_exp_singularity(self, batch_size: int = 100):
+        """
+        Tests whether the `se3_exp_map` is robust to the input vectors
+        with low L2 norms, where the algorithm is numerically unstable.
+        """
+        # generate random log-rotations with a tiny angle
+        log_rot = TestSE3.init_log_transform(batch_size=batch_size)
+        log_rot_small = log_rot * 1e-6
+        log_rot_small.requires_grad = True
+        transforms = se3_exp_map(log_rot_small)
+        # tests whether all outputs are finite
+        self.assertTrue(torch.isfinite(transforms).all())
+        # tests whether all gradients are finite and not None
+        loss = transforms.sum()
+        loss.backward()
+        self.assertIsNotNone(log_rot_small.grad)
+        self.assertTrue(torch.isfinite(log_rot_small.grad).all())
+
+    def test_se3_log_singularity(self, batch_size: int = 100):
+        """
+        Tests whether the `se3_log_map` is robust to the input matrices
+        whose rotation angles and translations are close to the numerically
+        unstable region (i.e. matrices with low rotation angles
+        and 0 translation).
+        """
+        # generate random rotations with a tiny angle
+        device = torch.device("cuda:0")
+        identity = torch.eye(3, device=device)
+        rot180 = identity * torch.tensor([[1.0, -1.0, -1.0]], device=device)
+        r = [identity, rot180]
+        r.extend(
+            [
+                torch.qr(identity + torch.randn_like(identity) * 1e-6)[0]
+                + float(i > batch_size // 2) * (0.5 - torch.rand_like(identity)) * 1e-8
+                # this adds random noise to the second half
+                # of the random orthogonal matrices to generate
+                # near-orthogonal matrices
+                for i in range(batch_size - 2)
+            ]
+        )
+        r = torch.stack(r)
+        # tiny translations
+        t = torch.randn(batch_size, 3, dtype=r.dtype, device=device) * 1e-6
+        # create the transform matrix
+        transform = torch.zeros(batch_size, 4, 4, dtype=torch.float32, device=device)
+        transform[:, :3, :3] = r
+        transform[:, 3, :3] = t
+        transform[:, 3, 3] = 1.0
+        transform.requires_grad = True
+        # the log of the transform
+        log_transform = se3_log_map(transform, eps=1e-4, cos_bound=1e-4)
+        # tests whether all outputs are finite
+        self.assertTrue(torch.isfinite(log_transform).all())
+        # tests whether all gradients are finite and not None
+        loss = log_transform.sum()
+        loss.backward()
+        self.assertIsNotNone(transform.grad)
+        self.assertTrue(torch.isfinite(transform.grad).all())
+
+    def test_se3_exp_zero_translation(self, batch_size: int = 100):
+        """
+        Check that `se3_exp_map` with zero translation gives
+        the same result as corresponding `so3_exp_map`.
+        """
+        log_transform = TestSE3.init_log_transform(batch_size=batch_size)
+        log_transform[:, :3] *= 0.0
+        transform = se3_exp_map(log_transform, eps=1e-8)
+        transform_so3 = so3_exp_map(log_transform[:, 3:], eps=1e-8)
+        self.assertClose(
+            transform[:, :3, :3], transform_so3.permute(0, 2, 1), atol=1e-4
+        )
+        self.assertClose(
+            transform[:, 3, :3], torch.zeros_like(transform[:, :3, 3]), atol=1e-4
+        )
+
+    def test_se3_log_zero_translation(self, batch_size: int = 100):
+        """
+        Check that `se3_log_map` with zero translation gives
+        the same result as corresponding `so3_log_map`.
+        """
+        transform = TestSE3.init_transform(batch_size=batch_size)
+        transform[:, 3, :3] *= 0.0
+        log_transform = se3_log_map(transform, eps=1e-8, cos_bound=1e-4)
+        log_transform_so3 = so3_log_map(transform[:, :3, :3], eps=1e-8, cos_bound=1e-4)
+        self.assertClose(log_transform[:, 3:], -log_transform_so3, atol=1e-4)
+        self.assertClose(
+            log_transform[:, :3], torch.zeros_like(log_transform[:, :3]), atol=1e-4
+        )
+
+    def test_se3_exp_to_log_to_exp(self, batch_size: int = 10000):
+        """
+        Check that `se3_exp_map(se3_log_map(A))==A` for
+        a batch of randomly generated SE(3) matrices `A`.
+        """
+        transform = TestSE3.init_transform(batch_size=batch_size)
+        # Limit test transforms to those not around the singularity where
+        # the rotation angle~=pi.
+        nonsingular = so3_rotation_angle(transform[:, :3, :3]) < 3.134
+        transform = transform[nonsingular]
+        transform_ = se3_exp_map(
+            se3_log_map(transform, eps=1e-8, cos_bound=0.0), eps=1e-8
+        )
+        self.assertClose(transform, transform_, atol=0.02)
+
+    def test_se3_log_to_exp_to_log(self, batch_size: int = 100):
+        """
+        Check that `se3_log_map(se3_exp_map(log_transform))==log_transform`
+        for a randomly generated batch of SE(3) matrix logarithms `log_transform`.
+        """
+        log_transform = TestSE3.init_log_transform(batch_size=batch_size)
+        log_transform_ = se3_log_map(se3_exp_map(log_transform, eps=1e-8), eps=1e-8)
+        self.assertClose(log_transform, log_transform_, atol=1e-1)
+
+    def test_bad_se3_input_value_err(self):
+        """
+        Tests whether `se3_exp_map` and `se3_log_map` correctly return
+        a ValueError if called with an argument of incorrect shape, or with
+        an tensor containing illegal values.
+        """
+        device = torch.device("cuda:0")
+
+        for size in ([5, 4], [3, 4, 5], [3, 5, 6]):
+            log_transform = torch.randn(size=size, device=device)
+            with self.assertRaises(ValueError):
+                se3_exp_map(log_transform)
+
+        for size in ([5, 4], [3, 4, 5], [3, 5, 6], [2, 2, 3, 4]):
+            transform = torch.randn(size=size, device=device)
+            with self.assertRaises(ValueError):
+                se3_log_map(transform)
+
+        # Test the case where transform[:, :, :3] != 0.
+        transform = torch.rand(size=[5, 4, 4], device=device) + 0.1
+        with self.assertRaises(ValueError):
+            se3_log_map(transform)
+
+    @staticmethod
+    def se3_expmap(batch_size: int = 10):
+        log_transform = TestSE3.init_log_transform(batch_size=batch_size)
+        torch.cuda.synchronize()
+
+        def compute_transforms():
+            se3_exp_map(log_transform)
+            torch.cuda.synchronize()
+
+        return compute_transforms
+
+    @staticmethod
+    def se3_logmap(batch_size: int = 10):
+        log_transform = TestSE3.init_transform(batch_size=batch_size)
+        torch.cuda.synchronize()
+
+        def compute_logs():
+            se3_log_map(log_transform)
+            torch.cuda.synchronize()
+
+        return compute_logs