３Dデータを保存したい

python
1コード
2import open3d as o3d
3import numpy as np
4import copy
5
6
7def draw_registration_result(source, target, transformation):
8    source_temp = copy.deepcopy(source)
9    target_temp = copy.deepcopy(target)
10    source_temp.paint_uniform_color([1, 0.706, 0])
11    target_temp.paint_uniform_color([0, 0.651, 0.929])
12    source_temp.transform(transformation)
13    o3d.visualization.draw_geometries([source_temp, target_temp])
14
15
16def preprocess_point_cloud(pcd, voxel_size):
17    #print(":: Downsample with a voxel size %.3f." % voxel_size)
18    pcd_down = pcd.voxel_down_sample(voxel_size)
19
20    radius_normal = voxel_size * 2
21    #print(":: Estimate normal with search radius %.3f." % radius_normal)
22    pcd_down.estimate_normals(
23        o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))
24    radius_feature = voxel_size * 5
25    #print(":: Compute FPFH feature with search radius %.3f." % radius_feature)
26    pcd_fpfh = o3d.registration.compute_fpfh_feature(
27        pcd_down,
28        o3d.geometry.KDTreeSearchParamHybrid(radius=radius_feature, max_nn=100))
29    return pcd_down, pcd_fpfh
30
31
32def prepare_dataset(voxel_size):
33    #print(":: Load two point clouds and disturb initial pose.")
34    source = o3d.io.read_point_cloud("/Users/****/Desktop/pcd-0.ply")
35    target = o3d.io.read_point_cloud("/Users/****/Desktop/pcd-1.ply")
36    trans_init = np.asarray([[0.0, 0.0, 1.0, 0.0], [1.0, 0.0, 0.0, 0.0],
37                             [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
38    source.transform(trans_init)
39    draw_registration_result(source, target, np.identity(4))
40
41    source_down, source_fpfh = preprocess_point_cloud(source, voxel_size)
42    target_down, target_fpfh = preprocess_point_cloud(target, voxel_size)
43    return source, target, source_down, target_down, source_fpfh, target_fpfh
44
45
46def execute_global_registration(source_down, target_down, source_fpfh,
47                                target_fpfh, voxel_size):
48    distance_threshold = voxel_size * 1.5
49    #print(":: RANSAC registration on downsampled point clouds.")
50    #print("   Since the downsampling voxel size is %.3f," % voxel_size)
51    #print("   we use a liberal distance threshold %.3f." % distance_threshold)
52    result = o3d.registration.registration_ransac_based_on_feature_matching(
53        source_down, target_down, source_fpfh, target_fpfh, distance_threshold,
54        o3d.registration.TransformationEstimationPointToPoint(False), 4, [
55            o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
56            o3d.registration.CorrespondenceCheckerBasedOnDistance(
57                distance_threshold)
58        ], o3d.registration.RANSACConvergenceCriteria(4000000, 500))
59    return result
60
61
62def refine_registration(source, target, source_fpfh, target_fpfh, voxel_size):
63    distance_threshold = voxel_size * 0.4
64    #print(":: Point-to-plane ICP registration is applied on original point")
65    #print("   clouds to refine the alignment. This time we use a strict")
66    #print("   distance threshold %.3f." % distance_threshold)
67    source.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
68    target.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
69    result = o3d.registration.registration_icp(
70        source, target, distance_threshold, result_ransac.transformation,
71        o3d.registration.TransformationEstimationPointToPlane())
72    return result
73
74num =0
75if __name__ == "__main__":
76    voxel_size = 0.05  # means 5cm for the dataset
77    source, target, source_down, target_down, source_fpfh, target_fpfh = \
78            prepare_dataset(voxel_size)
79
80    result_ransac = execute_global_registration(source_down, target_down,
81                                                source_fpfh, target_fpfh,
82                                                voxel_size)
83
84
85    #print(result_ransac)
86    draw_registration_result(source_down, target_down,
87                             result_ransac.transformation)
88
89    result_icp = refine_registration(source, target, source_fpfh, target_fpfh,
90                                     voxel_size)
91
92    print(result_icp)
93    draw_registration_result(source, target, result_icp.transformation)
94
95
96
97    print("Saving to {0}.ply...")
98    pcd = o3d.geometry.PointCloud(result_icp)
99    o3d.io.write_point_cloud("/Users/****/Desktop/out.ply", pcd)
100
101    print("Done")
102
103```### 前提・実現したいこと
104realsenseL515 を用いて、対象物１つをさまざまな角度から撮影して3Dデータ(plyファイル)を取得した。
105それらの複数の3Dデータを一つに重ね合わせ、plyファイルまたはcsvファイルに出力したい。
106
107
108
1093Dデータを重ね合わせること自体は実行できます。
110しかし、データを保存する際エラーが出てしまいます。
111### 発生している問題・エラーメッセージ
112

エラーメッセージ
pcd = o3d.geometry.PointCloud(result_icp)
TypeError: init(): incompatible constructor arguments. The following argument types are supported:
1. open3d.open3d_pybind.geometry.PointCloud()
2. open3d.open3d_pybind.geometry.PointCloud(arg0: open3d.open3d_pybind.geometry.PointCloud)
3. open3d.open3d_pybind.geometry.PointCloud(points: open3d.open3d_pybind.utility.Vector3dVector)

Invoked with: registration::RegistrationResult with fitness=4.901549e-01, inlier_rmse=9.731451e-03, and correspondence_set size of 68357
Access transformation to get result.

python

### 試したこと
エラーメッセージから保存するデータのタイプが違うとのことで、タイプを変換する関数などを探してみましたが、あまりヒットせず、詰まっています。


### 補足情報（FW/ツールのバージョンなど）
OS:Mac
python3.7
を使用しています

ここにより詳細な情報を記載してください。