def convert(og_mesh, voxel):
transforms = tfs.Compose([mesh_conversion,
tfs.MeshLaplacianSmoothing(smoothing_iterations)])
new_mesh = transforms(voxel)
new_mesh.vertices = pcfunc.realign(new_mesh.vertices, og_mesh.vertices)
return {'vertices': new_mesh.vertices, 'faces': new_mesh.faces}
def test_ModelNetPointCloud(device):
transform = tfs.Compose([
tfs.TriangleMeshToPointCloud(num_samples=32),
tfs.NormalizePointCloud()
])
models = kal.datasets.ModelNet(basedir=MODELNET_ROOT,
categories=['bathtub'],
split='test',
transform=transform)
assert len(models) == 50
for obj, category in models:
assert category.item() == 0
assert isinstance(obj, torch.Tensor)
assert obj.size(0) == 32
