def test_mesh_normal_consistency(self):
"""
Test Mesh Normal Consistency for random meshes.
"""
meshes = TestMeshNormalConsistency.init_meshes(5, 100, 300)
out1 = mesh_normal_consistency(meshes)
out2 = TestMeshNormalConsistency.mesh_normal_consistency_naive(meshes)
self.assertTrue(torch.allclose(out1, out2))
def test_no_intersection(self):
"""
Test Mesh Normal Consistency for a mesh known to have no
intersecting faces.
"""
verts = torch.rand(1, 6, 3)
faces = torch.arange(6).reshape(1, 2, 3)
meshes = Meshes(verts=verts, faces=faces)
out = mesh_normal_consistency(meshes)
self.assertEqual(out.item(), 0)
def loss():
mesh_normal_consistency(meshes)
torch.cuda.synchronize()
def test_mesh_normal_consistency_simple(self):
r"""
Mesh 1:
v3
/\
/ \
e4 / f1 \ e3
/ \
v2 /___e2___\ v1
\ /
\ /
e1 \ f0 / e0
\ /
\/
v0
"""
device = torch.device("cuda:0")
# mesh1 shown above
verts1 = torch.rand((4, 3), dtype=torch.float32, device=device)
faces1 = torch.tensor([[0, 1, 2], [2, 1, 3]],
dtype=torch.int64,
device=device)
# mesh2 is a cuboid with 8 verts, 12 faces and 18 edges
verts2 = torch.tensor(
[
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
],
dtype=torch.float32,
device=device,
)
faces2 = torch.tensor(
[
[0, 1, 2],
[1, 3, 2], # left face: 0, 1
[2, 3, 6],
[3, 7, 6], # bottom face: 2, 3
[0, 2, 6],
[0, 6, 4], # front face: 4, 5
[0, 5, 1],
[0, 4, 5], # up face: 6, 7
[6, 7, 5],
[6, 5, 4], # right face: 8, 9
[1, 7, 3],
[1, 5, 7], # back face: 10, 11
],
dtype=torch.int64,
device=device,
)
# mesh3 is like mesh1 but with another face added to e2
verts3 = torch.rand((5, 3), dtype=torch.float32, device=device)
faces3 = torch.tensor([[0, 1, 2], [2, 1, 3], [2, 1, 4]],
dtype=torch.int64,
device=device)
meshes = Meshes(verts=[verts1, verts2, verts3],
faces=[faces1, faces2, faces3])
# mesh1: normal consistency computation
n0 = (verts1[1] - verts1[2]).cross(verts1[3] - verts1[2])
n1 = (verts1[1] - verts1[2]).cross(verts1[0] - verts1[2])
loss1 = 1.0 - torch.cosine_similarity(n0.view(1, 3), -(n1.view(1, 3)))
# mesh2: normal consistency computation
# In the cube mesh, 6 edges are shared with coplanar faces (loss=0),
# 12 edges are shared by perpendicular faces (loss=1)
loss2 = 12.0 / 18
# mesh3
n0 = (verts3[1] - verts3[2]).cross(verts3[3] - verts3[2])
n1 = (verts3[1] - verts3[2]).cross(verts3[0] - verts3[2])
n2 = (verts3[1] - verts3[2]).cross(verts3[4] - verts3[2])
loss3 = (3.0 -
torch.cosine_similarity(n0.view(1, 3), -(n1.view(1, 3))) -
torch.cosine_similarity(n0.view(1, 3), -(n2.view(1, 3))) -
torch.cosine_similarity(n1.view(1, 3), -(n2.view(1, 3))))
loss3 /= 3.0
loss = (loss1 + loss2 + loss3) / 3.0
out = mesh_normal_consistency(meshes)
self.assertTrue(torch.allclose(out, loss))
