从点云优化三维曲面重建

Question

我是o3d新手，我希望有人根据我的代码带我四处看看：） 我正试图从（少数）实验数据重建一个曲面。我希望有尽可能多的灵活性/可调性

我的代码是这样的：

import open3d as o3d
sys.path.append('..')

output_path=(r"C:\Users\Giammarco\Desktop\PYTHON_graphs\OUTPUTS\\")

poisson_mesh=[]
densities=[]

pcd = o3d.geometry.PointCloud()
pcd.normals = o3d.utility.Vector3dVector(np.zeros((1, 3)))  # invalidate existing normals

#load the point cloud
point_cloud=np.array([x,y,z]).T
cloud = PyntCloud.from_instance("open3d", pcd)
pcd.points = o3d.utility.Vector3dVector(point_cloud)
#resise the scale of the sample
vox_grid = o3d.geometry.VoxelGrid.create_from_point_cloud(pcd, 1.)

#presetn in all approaches of plc
kdtree = cloud.add_structure("kdtree")
testc = cloud.get_neighbors(k=5)
distances = pcd.compute_nearest_neighbor_distance()
avg_dist = np.mean(distances)

#compute the normals
pcd.estimate_normals(); #mandatory
#orient the normals
#Number of nearest neighbours: 5 is the minimum to have a closed surface with scale >= 2
pcd.orient_normals_consistent_tangent_plane(7)

#Poisson algorithm
poisson_mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=9, width=0, scale=3.5, linear_fit=False)
bbox = pcd.get_axis_aligned_bounding_box()
p_mesh_crop = poisson_mesh.crop(bbox)

# cleaning
# p_mesh_crop =poisson_mesh.simplify_quadric_decimation(6000)
# p_mesh_crop.remove_unreferenced_vertices
# p_mesh_crop.remove_degenerate_triangles()
# p_mesh_crop.remove_duplicated_triangles()
# p_mesh_crop.remove_duplicated_vertices()
# p_mesh_crop.remove_non_manifold_edges()

#designing the surface colour
#densities are the real density of features
densities = np.asarray(densities)
density_colors = plt.get_cmap('viridis')((dgo - dgo.min()) / (dgo.max() - dgo.min()))
density_colors = density_colors[:, :3]

#works for the plotting in o3d
poisson_mesh.vertex_colors = o3d.utility.Vector3dVector(density_colors)


o3d.io.write_triangle_mesh(output_path+"bpa_mesh.ply", dec_mesh);
o3d.io.write_triangle_mesh(output_path+"p_mesh_c.ply", poisson_mesh);
# o3d.io.write_triangle_mesh(output_path+"p_mesh_c.ply", p_mesh_crop);

# my_lods = lod_mesh_export(p_mesh_crop, [100000,50000,10000,1000,100], ".ply", output_path)
my_lods = lod_mesh_export(poisson_mesh, [100000,50000,10000,1000,100], ".ply", output_path)

# o3d.visualization.draw_geometries([pcd, p_mesh_crop], mesh_show_back_face=True)
# o3d.visualization.draw_geometries([pcd, poisson_mesh],mesh_show_back_face=True)
# o3d.visualization.draw_geometries([pcd, poisson_mesh[100000]],point_show_normal=True)

# tri_mesh_pois.show()#designing the surface colour
#densities are the real density of features
densities = np.asarray(densities)
density_colors = plt.get_cmap('viridis')((dgo - dgo.min()) / (dgo.max() - dgo.min()))
density_colors = density_colors[:, :3]

#works for the plotting in o3d
poisson_mesh.vertex_colors = o3d.utility.Vector3dVector(density_colors)
o3d.io.write_triangle_mesh(output_path+"p_mesh_c.ply", poisson_mesh);
my_lods = lod_mesh_export(poisson_mesh, [100000,50000,10000,1000,100], ".ply", output_path)

#SHORTCUTS from keyboard: n = show normals, q = quit, w = mesh
o3d.visualization.draw_geometries([pcd, poisson_mesh],mesh_show_back_face=True)

一些产出：

1. 我关心的是create_from_point_cloud_poissonfit模型选项：除了depth和size之外，还有什么方法可以调整它的参数吗？我是否应该设置一个迭代过程来实现更好的转换（例如阈值）？正如你所看到的，计算表面和实验点之间的距离非常大

2. 法线的估计是否正确设置？在第二个输出中，某些方向仍然是非常随机的。 我也尝试过这种语法：pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.05,max_nn=20));，但它不会收敛到一个闭合曲面，只是一个平面（见下文）

请给我反馈我的代码和如何改进它的建议

谢谢你的支持