diff --git a/pytorch3d/common/compat.py b/pytorch3d/common/compat.py
index 278ecb24..e15a6107 100644
--- a/pytorch3d/common/compat.py
+++ b/pytorch3d/common/compat.py
@@ -49,3 +49,12 @@ def qr(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:  # pragma: no cove
         # PyTorch version >= 1.9
         return torch.linalg.qr(A)
     return torch.qr(A)
+
+
+def eigh(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:  # pragma: no cover
+    """
+    Like torch.linalg.eigh, assuming the argument is a symmetric real matrix.
+    """
+    if hasattr(torch, "linalg") and hasattr(torch.linalg, "eigh"):
+        return torch.linalg.eigh(A)
+    return torch.symeig(A, eigenvalues=True)
diff --git a/pytorch3d/ops/perspective_n_points.py b/pytorch3d/ops/perspective_n_points.py
index 017c345b..92cf4dfb 100644
--- a/pytorch3d/ops/perspective_n_points.py
+++ b/pytorch3d/ops/perspective_n_points.py
@@ -16,6 +16,7 @@ from typing import NamedTuple, Optional
 
 import torch
 import torch.nn.functional as F
+from pytorch3d.common.compat import eigh
 from pytorch3d.ops import points_alignment, utils as oputil
 
 
@@ -105,7 +106,7 @@ def _null_space(m, kernel_dim):
             kernel vectors, of size B x kernel_dim
     """
     mTm = torch.bmm(m.transpose(1, 2), m)
-    s, v = torch.symeig(mTm, eigenvectors=True)
+    s, v = eigh(mTm)
     return v[:, :, :kernel_dim].reshape(-1, 4, 3, kernel_dim), s[:, :kernel_dim]
 
 
diff --git a/pytorch3d/ops/points_normals.py b/pytorch3d/ops/points_normals.py
index 702c0bb7..70128814 100644
--- a/pytorch3d/ops/points_normals.py
+++ b/pytorch3d/ops/points_normals.py
@@ -7,8 +7,9 @@
 from typing import TYPE_CHECKING, Tuple, Union
 
 import torch
+from pytorch3d.common.compat import eigh
+from pytorch3d.common.workaround import symeig3x3
 
-from ..common.workaround import symeig3x3
 from .utils import convert_pointclouds_to_tensor, get_point_covariances
 
 
@@ -139,14 +140,14 @@ def estimate_pointcloud_local_coord_frames(
 
     # get the local coord frames as principal directions of
     # the per-point covariance
-    # this is done with torch.symeig, which returns the
+    # this is done with torch.symeig / torch.linalg.eigh, which returns the
     # eigenvectors (=principal directions) in an ascending order of their
-    # corresponding eigenvalues, while the smallest eigenvalue's eigenvector
-    # corresponds to the normal direction
+    # corresponding eigenvalues, and the smallest eigenvalue's eigenvector
+    # corresponds to the normal direction; or with a custom equivalent.
     if use_symeig_workaround:
         curvatures, local_coord_frames = symeig3x3(cov, eigenvectors=True)
     else:
-        curvatures, local_coord_frames = torch.symeig(cov, eigenvectors=True)
+        curvatures, local_coord_frames = eigh(cov)
 
     # disambiguate the directions of individual principal vectors
     if disambiguate_directions: