use workaround for points_normals

Summary:
Use existing workaround for batched 3x3 symeig because it is faster than torch.symeig.

Added benchmark showing speedup. True = workaround.
```
Benchmark                Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
normals_True_3000            16237           17233             31
normals_True_6000            33028           33391             16
normals_False_3000        18623069        18623069              1
normals_False_6000        36535475        36535475              1
```

Should help https://github.com/facebookresearch/pytorch3d/issues/988

Reviewed By: nikhilaravi

Differential Revision: D33660585

fbshipit-source-id: d1162b277f5d61ed67e367057a61f25e03888dce

pytorch3d/ops/points_normals.py

@@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Tuple, Union
 
 import torch
 
+from ..common.workaround import symeig3x3
 from .utils import convert_pointclouds_to_tensor, get_point_covariances
 
 
@@ -19,6 +20,8 @@ def estimate_pointcloud_normals(
     pointclouds: Union[torch.Tensor, "Pointclouds"],
     neighborhood_size: int = 50,
     disambiguate_directions: bool = True,
+    *,
+    use_symeig_workaround: bool = True,
 ) -> torch.Tensor:
     """
     Estimates the normals of a batch of `pointclouds`.
@@ -33,6 +36,8 @@ def estimate_pointcloud_normals(
         geometry around each point.
       **disambiguate_directions**: If `True`, uses the algorithm from [1] to
         ensure sign consistency of the normals of neighboring points.
+      **use_symeig_workaround**: If `True`, uses a custom eigenvalue
+        calculation.
 
     Returns:
       **normals**: A tensor of normals for each input point
@@ -48,6 +53,7 @@ def estimate_pointcloud_normals(
         pointclouds,
         neighborhood_size=neighborhood_size,
         disambiguate_directions=disambiguate_directions,
+        use_symeig_workaround=use_symeig_workaround,
     )
 
     # the normals correspond to the first vector of each local coord frame
@@ -60,6 +66,8 @@ def estimate_pointcloud_local_coord_frames(
     pointclouds: Union[torch.Tensor, "Pointclouds"],
     neighborhood_size: int = 50,
     disambiguate_directions: bool = True,
+    *,
+    use_symeig_workaround: bool = True,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     Estimates the principal directions of curvature (which includes normals)
@@ -88,6 +96,8 @@ def estimate_pointcloud_local_coord_frames(
         geometry around each point.
       **disambiguate_directions**: If `True`, uses the algorithm from [1] to
         ensure sign consistency of the normals of neighboring points.
+      **use_symeig_workaround**: If `True`, uses a custom eigenvalue
+        calculation.
 
     Returns:
       **curvatures**: The three principal curvatures of each point
@@ -133,7 +143,10 @@ def estimate_pointcloud_local_coord_frames(
     # eigenvectors (=principal directions) in an ascending order of their
     # corresponding eigenvalues, while the smallest eigenvalue's eigenvector
     # corresponds to the normal direction
-    curvatures, local_coord_frames = torch.symeig(cov, eigenvectors=True)
+    if use_symeig_workaround:
+        curvatures, local_coord_frames = symeig3x3(cov, eigenvectors=True)
+    else:
+        curvatures, local_coord_frames = torch.symeig(cov, eigenvectors=True)
 
     # disambiguate the directions of individual principal vectors
     if disambiguate_directions: