Mesh normal consistency when many faces intersect

Summary: We were double counting some pairs in some cases. Specifically if four or more faces share an edge, then some of them were getting double counted. This is a minimal tweak to avoid that.

Reviewed By: nikhilaravi

Differential Revision: D26073477

fbshipit-source-id: a40032acf3044bb98dd91cb29904614ef64d5599

pytorch3d/loss/mesh_normal_consistency.py

@@ -108,7 +108,7 @@ def mesh_normal_consistency(meshes):
             for e in vert_edge_pair_idx
             for i in range(len(e) - 1)
             for j in range(1, len(e))
-            if i != j
+            if i < j
         ]
         vert_edge_pair_idx = torch.tensor(
             vert_edge_pair_idx, device=meshes.device, dtype=torch.int64

tests/test_mesh_normal_consistency.py

@@ -10,6 +10,9 @@ from pytorch3d.utils.ico_sphere import ico_sphere
 
 
 class TestMeshNormalConsistency(unittest.TestCase):
+    def setUp(self) -> None:
+        torch.manual_seed(42)
+
     @staticmethod
     def init_faces(num_verts: int = 1000):
         faces = []
@@ -95,17 +98,16 @@ class TestMeshNormalConsistency(unittest.TestCase):
                 v2 = verts_packed[v2]
                 normals.append((v1 - v0).view(-1).cross((v2 - v0).view(-1)))
             for i in range(len(normals) - 1):
-                for j in range(1, len(normals)):
-                    if i != j:
-                        mesh_idx.append(edges_packed_to_mesh_idx[e])
-                        loss.append(
-                            (
-                                1
-                                - torch.cosine_similarity(
-                                    normals[i].view(1, 3), -normals[j].view(1, 3)
-                                )
+                for j in range(i + 1, len(normals)):
+                    mesh_idx.append(edges_packed_to_mesh_idx[e])
+                    loss.append(
+                        (
+                            1
+                            - torch.cosine_similarity(
+                                normals[i].view(1, 3), -normals[j].view(1, 3)
                             )
                         )
+                    )
 
         mesh_idx = torch.tensor(mesh_idx, device=meshes.device)
         num = mesh_idx.bincount(minlength=N)