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tests/test_mesh_normal_consistency.py
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244
tests/test_mesh_normal_consistency.py
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#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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import unittest
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import torch
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from pytorch3d.loss.mesh_normal_consistency import mesh_normal_consistency
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from pytorch3d.structures.meshes import Meshes
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from pytorch3d.utils.ico_sphere import ico_sphere
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class TestMeshNormalConsistency(unittest.TestCase):
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@staticmethod
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def init_faces(num_verts: int = 1000):
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faces = []
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for f0 in range(num_verts):
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for f1 in range(f0 + 1, num_verts):
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f2 = torch.arange(f1 + 1, num_verts)
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n = f2.shape[0]
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if n == 0:
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continue
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faces.append(
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torch.stack(
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[
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torch.full((n,), f0, dtype=torch.int64),
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torch.full((n,), f1, dtype=torch.int64),
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f2,
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],
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dim=1,
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)
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)
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faces = torch.cat(faces, 0)
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return faces
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@staticmethod
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def init_meshes(
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num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
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):
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device = torch.device("cuda:0")
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valid_faces = TestMeshNormalConsistency.init_faces(num_verts).to(device)
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verts_list = []
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faces_list = []
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for _ in range(num_meshes):
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verts = (
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torch.rand((num_verts, 3), dtype=torch.float32, device=device)
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* 2.0
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- 1.0
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) # verts in the space of [-1, 1]
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"""
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faces = torch.stack(
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[
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torch.randperm(num_verts, device=device)[:3]
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for _ in range(num_faces)
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],
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dim=0,
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)
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# avoids duplicate vertices in a face
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"""
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idx = torch.randperm(valid_faces.shape[0], device=device)[
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: min(valid_faces.shape[0], num_faces)
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]
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faces = valid_faces[idx]
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verts_list.append(verts)
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faces_list.append(faces)
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meshes = Meshes(verts_list, faces_list)
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return meshes
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@staticmethod
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def mesh_normal_consistency_naive(meshes):
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"""
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Naive iterative implementation of mesh normal consistency.
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"""
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N = len(meshes)
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verts_packed = meshes.verts_packed()
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faces_packed = meshes.faces_packed()
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edges_packed = meshes.edges_packed()
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face_to_edge = meshes.faces_packed_to_edges_packed()
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edges_packed_to_mesh_idx = meshes.edges_packed_to_mesh_idx()
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E = edges_packed.shape[0]
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loss = []
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mesh_idx = []
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for e in range(E):
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face_idx = face_to_edge.eq(e).any(1).nonzero() # indexed to faces
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v0 = verts_packed[edges_packed[e, 0]]
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v1 = verts_packed[edges_packed[e, 1]]
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normals = []
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for f in face_idx:
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v2 = -1
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for j in range(3):
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if (
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faces_packed[f, j] != edges_packed[e, 0]
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and faces_packed[f, j] != edges_packed[e, 1]
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):
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v2 = faces_packed[f, j]
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assert v2 > -1
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v2 = verts_packed[v2]
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normals.append((v1 - v0).view(-1).cross((v2 - v0).view(-1)))
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for i in range(len(normals) - 1):
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for j in range(1, len(normals)):
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if i != j:
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mesh_idx.append(edges_packed_to_mesh_idx[e])
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loss.append(
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(
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1
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- torch.cosine_similarity(
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normals[i].view(1, 3),
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-normals[j].view(1, 3),
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)
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)
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)
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mesh_idx = torch.tensor(mesh_idx, device=meshes.device)
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num = mesh_idx.bincount(minlength=N)
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weights = 1.0 / num[mesh_idx].float()
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loss = torch.cat(loss) * weights
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return loss.sum() / N
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def test_mesh_normal_consistency_simple(self):
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r"""
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Mesh 1:
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v3
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/\
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/ \
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e4 / f1 \ e3
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/ \
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v2 /___e2___\ v1
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\ /
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\ /
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e1 \ f0 / e0
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\ /
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\/
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v0
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"""
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device = torch.device("cuda:0")
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# mesh1 shown above
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verts1 = torch.rand((4, 3), dtype=torch.float32, device=device)
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faces1 = torch.tensor(
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[[0, 1, 2], [2, 1, 3]], dtype=torch.int64, device=device
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)
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# mesh2 is a cuboid with 8 verts, 12 faces and 18 edges
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verts2 = torch.tensor(
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[
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[0, 0, 0],
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[0, 0, 1],
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[0, 1, 0],
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[0, 1, 1],
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[1, 0, 0],
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[1, 0, 1],
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[1, 1, 0],
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[1, 1, 1],
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],
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dtype=torch.float32,
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device=device,
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)
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faces2 = torch.tensor(
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[
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[0, 1, 2],
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[1, 3, 2], # left face: 0, 1
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[2, 3, 6],
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[3, 7, 6], # bottom face: 2, 3
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[0, 2, 6],
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[0, 6, 4], # front face: 4, 5
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[0, 5, 1],
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[0, 4, 5], # up face: 6, 7
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[6, 7, 5],
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[6, 5, 4], # right face: 8, 9
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[1, 7, 3],
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[1, 5, 7], # back face: 10, 11
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],
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dtype=torch.int64,
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device=device,
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)
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# mesh3 is like mesh1 but with another face added to e2
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verts3 = torch.rand((5, 3), dtype=torch.float32, device=device)
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faces3 = torch.tensor(
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[[0, 1, 2], [2, 1, 3], [2, 1, 4]], dtype=torch.int64, device=device
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)
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meshes = Meshes(
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verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3]
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)
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# mesh1: normal consistency computation
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n0 = (verts1[1] - verts1[2]).cross(verts1[3] - verts1[2])
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n1 = (verts1[1] - verts1[2]).cross(verts1[0] - verts1[2])
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loss1 = 1.0 - torch.cosine_similarity(n0.view(1, 3), -n1.view(1, 3))
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# mesh2: normal consistency computation
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# In the cube mesh, 6 edges are shared with coplanar faces (loss=0),
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# 12 edges are shared by perpendicular faces (loss=1)
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loss2 = 12.0 / 18
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# mesh3
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n0 = (verts3[1] - verts3[2]).cross(verts3[3] - verts3[2])
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n1 = (verts3[1] - verts3[2]).cross(verts3[0] - verts3[2])
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n2 = (verts3[1] - verts3[2]).cross(verts3[4] - verts3[2])
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loss3 = (
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3.0
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- torch.cosine_similarity(n0.view(1, 3), -n1.view(1, 3))
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- torch.cosine_similarity(n0.view(1, 3), -n2.view(1, 3))
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- torch.cosine_similarity(n1.view(1, 3), -n2.view(1, 3))
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)
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loss3 /= 3.0
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loss = (loss1 + loss2 + loss3) / 3.0
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out = mesh_normal_consistency(meshes)
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self.assertTrue(torch.allclose(out, loss))
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def test_mesh_normal_consistency(self):
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"""
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Test Mesh Normal Consistency for random meshes.
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"""
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meshes = TestMeshNormalConsistency.init_meshes(5, 100, 300)
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out1 = mesh_normal_consistency(meshes)
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out2 = TestMeshNormalConsistency.mesh_normal_consistency_naive(meshes)
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self.assertTrue(torch.allclose(out1, out2))
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@staticmethod
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def mesh_normal_consistency_with_ico(
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num_meshes: int, level: int = 3, device: str = "cpu"
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):
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device = torch.device(device)
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mesh = ico_sphere(level, device)
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verts, faces = mesh.get_mesh_verts_faces(0)
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verts_list = [verts.clone() for _ in range(num_meshes)]
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faces_list = [faces.clone() for _ in range(num_meshes)]
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meshes = Meshes(verts_list, faces_list)
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torch.cuda.synchronize()
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def loss():
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mesh_normal_consistency(meshes)
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torch.cuda.synchronize()
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return loss
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