def setUp(self):
pcd = o3.io.read_point_cloud('data/horse.ply')
pcd = pcd.voxel_down_sample(voxel_size=0.01)
self._source = np.asarray(pcd.points)
rot = trans.euler_matrix(*np.random.uniform(0.0, np.pi / 4, 3))
self._tf = tf.RigidTransformation(rot[:3, :3], np.zeros(3))
self._target = self._tf.transform(self._source)
def setUp(self):
pcd = o3.read_point_cloud('data/horse.ply')
pcd = o3.voxel_down_sample(pcd, voxel_size=0.01)
estimate_normals(pcd, o3.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=10))
self._source = np.asarray(pcd.points)
rot = trans.euler_matrix(*np.random.uniform(0.0, np.pi / 4, 3))
self._tf = tf.RigidTransformation(rot[:3, :3], np.zeros(3))
self._target = self._tf.transform(self._source)
self._target_normals = np.asarray(np.dot(pcd.normals, self._tf.rot.T))
def setUp(self):
# Generate plane
points = []
normals = []
resolution = 0.2
for i in range(5):
for j in range(5):
points.append(
np.array(
[-0.5 + i * resolution, -0.5 + j * resolution, -0.5]))
normals.append(np.array([0.0, 0.0, 1.0]))
self._source = np.array(points)
rot = trans.euler_matrix(np.deg2rad(10.0), 0.0, 0.0)
self._tf = tf.RigidTransformation(rot[:3, :3], np.zeros(3))
self._target = self._tf.transform(self._source)
self._target_normals = np.dot(np.array(normals), self._tf.rot.T)
