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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
import open3d as o3d
import open3d.core as o3c
import numpy as np
import pytest
from open3d_test import list_devices
def get_pcds(dtype, device):
source_points = o3c.Tensor(
[[1.0, 0.5, 2.0], [0.5, 0.5, 2.0], [0.5, 0.5, 2.5], [3.0, 1.0, 1.5],
[3.5, 1.0, 1.0], [0.5, 1.0, 2.0], [1.5, 1.0, 1.5], [2.0, 1.0, 1.5],
[1.0, 1.0, 2.0], [2.5, 1.0, 1.5], [3.0, 1.0, 1.0], [0.5, 1.0, 2.5],
[1.0, 1.0, 1.0], [1.5, 1.0, 1.0], [1.0, 1.5, 1.0], [3.0, 1.5, 1.0],
[3.5, 1.5, 1.0], [3.0, 1.5, 1.5], [0.5, 1.5, 1.5], [0.5, 1.5, 2.0],
[1.0, 1.5, 2.0], [2.5, 1.5, 1.5], [1.5, 1.5, 1.0], [1.5, 1.5, 1.5],
[2.0, 1.5, 1.5], [3.0, 1.5, 0.5], [2.5, 1.5, 1.0], [2.0, 1.5, 1.0],
[3.0, 2.0, 0.5], [0.5, 2.0, 1.5], [3.0, 2.0, 1.0], [1.0, 2.0, 1.0],
[2.0, 1.5, 0.5], [0.5, 2.0, 2.0], [2.5, 2.0, 1.0], [2.5, 2.0, 0.5],
[2.0, 2.0, 0.5], [2.5, 1.5, 0.5], [3.0, 2.0, 1.5], [2.0, 2.0, 1.0],
[1.0, 2.0, 2.0], [1.5, 2.0, 1.0], [1.5, 2.0, 1.5], [2.5, 2.0, 1.5],
[2.0, 2.0, 1.5], [1.0, 2.0, 0.5], [0.5, 2.0, 1.0], [1.5, 2.0, 0.5],
[1.0, 2.0, 1.5]], dtype, device)
target_points = o3c.Tensor(
[[1.5, 1.0, 1.5], [2.5, 1.0, 1.5], [1.5, 1.0, 1.0], [1.0, 1.0, 1.0],
[2.0, 1.0, 1.5], [3.0, 1.0, 1.5], [1.0, 1.0, 0.5], [1.0, 1.5, 1.0],
[1.0, 1.5, 0.5], [1.0, 1.0, 1.5], [3.0, 1.0, 2.0], [3.0, 1.5, 2.0],
[3.0, 1.5, 1.5], [1.0, 1.5, 1.5], [1.5, 1.5, 1.5], [2.5, 1.5, 1.5],
[2.0, 1.5, 1.5], [1.5, 1.5, 1.0], [2.5, 1.5, 2.0], [1.0, 2.0, 1.0],
[1.0, 2.0, 0.5], [2.5, 1.5, 1.0], [3.0, 2.0, 1.5], [2.5, 2.0, 1.0],
[2.5, 2.0, 1.5], [1.5, 2.0, 1.0], [2.0, 1.5, 1.0], [1.0, 2.0, 1.5],
[2.0, 2.0, 1.0], [1.5, 2.0, 1.5], [1.5, 2.0, 0.5], [2.0, 2.0, 1.5],
[2.0, 2.0, 0.5], [1.5, 2.5, 1.0], [1.0, 2.5, 1.0], [3.0, 2.0, 1.0],
[2.0, 2.5, 1.0], [2.5, 2.5, 1.0]], dtype, device)
target_normals = o3c.Tensor(
[[0.15597, -0.0463812, -0.986672], [-0.213545, 0.887963, 0.407334],
[0.423193, -0.121977, -0.897792], [0.202251, 0.27611, -0.939605],
[0.275452, 0.207216, -0.938716], [0.326146, 0.0385317, -0.944534],
[0.983129, -0.174668, -0.0543011], [0.898665, -0.0602029, 0.434485],
[0.711325, 0.193223, -0.675783], [0.346158, 0.198724, -0.916888],
[0.302085, 0.28938, -0.908297], [0.341044, 0.414138, -0.843907],
[0.212191, 0.213068, -0.953717], [0.239759, 0.313187, -0.918929],
[0.302290, 0.27265, -0.913391], [0.209796, 0.402747, -0.890944],
[0.267025, 0.218226, -0.938656], [0.00126928, -0.976587, -0.21512],
[0.321912, 0.194736, -0.926526], [0.831227, 0.236675, -0.503037],
[0.987006, -0.155324, 0.0411639], [0.103384, -0.808796, -0.57893],
[0.181245, 0.66226, -0.727023], [0.235471, 0.525053, -0.817846],
[0.231954, 0.446165, -0.864369], [-0.261931, -0.725542, -0.636381],
[0.120953, -0.864985, -0.487003], [0.858345, -0.227847, 0.459706],
[-0.416259, -0.367408, -0.831709], [-0.476652, 0.206048, -0.854604],
[-0.211959, -0.523378, -0.825317], [-0.964914, 0.0541031, -0.256931],
[-0.0653566, -0.913961, -0.400504], [-0.846868, -0.170805, -0.503628],
[0.0366971, 0.515834, -0.855902], [-0.0714554, -0.855019, -0.513651],
[-0.0217377, -0.957744, -0.286799], [-0.0345231, -0.947096, -0.319088]
], dtype, device)
source = o3d.t.geometry.PointCloud(device)
target = o3d.t.geometry.PointCloud(device)
source.point.positions = source_points
target.point.positions = target_points
target.point.normals = target_normals
return source, target
@pytest.mark.parametrize("device", list_devices())
def test_icp_convergence_criteria_constructor(device):
# Constructor.
convergence_criteria = o3d.t.pipelines.registration.ICPConvergenceCriteria()
# Checking default values.
assert convergence_criteria.max_iteration == 30
assert convergence_criteria.relative_fitness == 1e-06
assert convergence_criteria.relative_rmse == 1e-06
@pytest.mark.parametrize("device", list_devices())
def test_registration_result_constructor(device):
dtype = o3c.float64
# Constructor.
registration_result = o3d.t.pipelines.registration.RegistrationResult()
# Checking default values.
assert registration_result.inlier_rmse == 0.0
assert registration_result.fitness == 0.0
assert registration_result.transformation.allclose(
o3c.Tensor.eye(4, dtype, o3c.Device("CPU:0")))
@pytest.mark.parametrize("device", list_devices())
def test_evaluate_registration(device):
supported_dtypes = [o3c.float32, o3c.float64]
for dtype in supported_dtypes:
source_t, target_t = get_pcds(dtype, device)
source_legacy = source_t.to_legacy()
target_legacy = target_t.to_legacy()
max_correspondence_distance = 3.0
init_trans_legacy = np.eye(4)
init_trans_t = o3c.Tensor.eye(4, o3c.float64, device)
evaluation_t = o3d.t.pipelines.registration.evaluate_registration(
source_t, target_t, max_correspondence_distance, init_trans_t)
evaluation_legacy = o3d.pipelines.registration.evaluate_registration(
source_legacy, target_legacy, max_correspondence_distance,
init_trans_legacy)
np.testing.assert_allclose(evaluation_t.inlier_rmse,
evaluation_legacy.inlier_rmse, 0.001)
np.testing.assert_allclose(evaluation_t.fitness,
evaluation_legacy.fitness, 0.001)
@pytest.mark.parametrize("device", list_devices())
def test_icp_point_to_point(device):
supported_dtypes = [o3c.float32, o3c.float64]
for dtype in supported_dtypes:
source_t, target_t = get_pcds(dtype, device)
source_legacy = source_t.to_legacy()
target_legacy = target_t.to_legacy()
max_correspondence_distance = 3.0
init_trans_legacy = np.array([[0.862, 0.011, -0.507, 0.5],
[-0.139, 0.967, -0.215, 0.7],
[0.487, 0.255, 0.835, -1.4],
[0.0, 0.0, 0.0, 1.0]])
init_trans_t = o3c.Tensor(init_trans_legacy,
dtype=o3c.float64,
device=device)
reg_p2p_t = o3d.t.pipelines.registration.icp(
source_t, target_t, max_correspondence_distance, init_trans_t,
o3d.t.pipelines.registration.TransformationEstimationPointToPoint(),
o3d.t.pipelines.registration.ICPConvergenceCriteria(
max_iteration=2))
reg_p2p_legacy = o3d.pipelines.registration.registration_icp(
source_legacy, target_legacy, max_correspondence_distance,
init_trans_legacy,
o3d.pipelines.registration.TransformationEstimationPointToPoint(),
o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=2))
np.testing.assert_allclose(reg_p2p_t.inlier_rmse,
reg_p2p_legacy.inlier_rmse, 0.001)
np.testing.assert_allclose(reg_p2p_t.fitness, reg_p2p_legacy.fitness,
0.001)
@pytest.mark.parametrize("device", list_devices())
def test_icp_point_to_plane(device):
supported_dtypes = [o3c.float32, o3c.float64]
for dtype in supported_dtypes:
source_t, target_t = get_pcds(dtype, device)
source_legacy = source_t.to_legacy()
target_legacy = target_t.to_legacy()
max_correspondence_distance = 3.0
init_trans_legacy = np.array([[0.862, 0.011, -0.507, 0.5],
[-0.139, 0.967, -0.215, 0.7],
[0.487, 0.255, 0.835, -1.4],
[0.0, 0.0, 0.0, 1.0]])
init_trans_t = o3c.Tensor(init_trans_legacy,
dtype=o3c.float64,
device=device)
reg_p2plane_t = o3d.t.pipelines.registration.icp(
source_t, target_t, max_correspondence_distance, init_trans_t,
o3d.t.pipelines.registration.TransformationEstimationPointToPlane(),
o3d.t.pipelines.registration.ICPConvergenceCriteria(
max_iteration=2))
reg_p2plane_legacy = o3d.pipelines.registration.registration_icp(
source_legacy, target_legacy, max_correspondence_distance,
init_trans_legacy,
o3d.pipelines.registration.TransformationEstimationPointToPlane(),
o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=2))
np.testing.assert_allclose(reg_p2plane_t.inlier_rmse,
reg_p2plane_legacy.inlier_rmse, 0.001)
np.testing.assert_allclose(reg_p2plane_t.fitness,
reg_p2plane_legacy.fitness, 0.001)
@pytest.mark.parametrize("device", list_devices())
def test_get_information_matrix(device):
supported_dtypes = [o3c.float32, o3c.float64]
for dtype in supported_dtypes:
source_t, target_t = get_pcds(dtype, device)
source_legacy = source_t.to_legacy()
target_legacy = target_t.to_legacy()
max_correspondence_distance = 3.0
transformation_legacy = np.array([[0.862, 0.011, -0.507, 0.5],
[-0.139, 0.967, -0.215, 0.7],
[0.487, 0.255, 0.835, -1.4],
[0.0, 0.0, 0.0, 1.0]])
transformation_t = o3c.Tensor(transformation_legacy,
dtype=o3c.float64,
device=device)
info_matrix_t = o3d.t.pipelines.registration.get_information_matrix(
source_t, target_t, max_correspondence_distance, transformation_t)
info_matrix_legacy = o3d.pipelines.registration.get_information_matrix_from_point_clouds(
source_legacy, target_legacy, max_correspondence_distance,
transformation_legacy)
np.testing.assert_allclose(info_matrix_t.cpu().numpy(),
info_matrix_legacy, 1e-1, 1e-1)
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