def convert(mesh):
points, face_choices = mesh_cvt.trianglemesh_to_pointcloud(
mesh, num_points)
face_normals = mesh.compute_face_normals()
point_normals = face_normals[face_choices]
return {'points': points, 'normals': point_normals}
import os
import pytest
import torch
from pathlib import Path
from kaolin.rep import TriangleMesh, VoxelGrid, PointCloud
from kaolin.conversions.meshconversions import trianglemesh_to_pointcloud, trianglemesh_to_voxelgrid
from kaolin.visualize.vis_usd import VisUsd
root = Path('tests/visualize/results')
root.mkdir(exist_ok=True)
mesh = TriangleMesh.from_obj('tests/model.obj')
voxels = VoxelGrid(trianglemesh_to_voxelgrid(mesh, 32))
pc = PointCloud(trianglemesh_to_pointcloud(mesh, 500)[0])
vis = VisUsd()
@pytest.mark.parametrize('object_3d', [mesh, voxels, pc])
@pytest.mark.parametrize('device', ['cpu', 'cuda'])
@pytest.mark.parametrize('meet_ground', [True, False])
@pytest.mark.parametrize('center_on_stage', [True, False])
@pytest.mark.parametrize('fit_to_stage', [True, False])
def test_vis(object_3d, device, meet_ground, center_on_stage, fit_to_stage):
if device == 'cuda':
if isinstance(object_3d, TriangleMesh):
object_3d.cuda()
elif isinstance(object_3d, PointCloud):
object_3d.points = object_3d.points.to(torch.device(device))
elif isinstance(object_3d, VoxelGrid):
object_3d.voxels = object_3d.voxels.to(torch.device(device))