Better surface reconstruction

#1

Hi Open3D community,

I am quite new here, I’d like someone to show me around based on my code :slight_smile:
I am trying to reconstruct a surface from (few) experimental data. I’d like to have as much flexibility/tunability as possible.

My code is something like this:

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)

output:

image

  1. I am concern about the create_from_point_cloud_poisson fit model option: is there a way to tune its parameters more than just depth and size? Is there an iterative process that I should set up for better conversion (e.g. threshold)? As you can see the distance btw the calculated surface and the experimental point is quite big.

  2. Is the estimation of normals properly set up? In the second output, some directions are still quite random.
    I tried this syntax too: pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.05,max_nn=20)); , but It doesn’t converge to a closed surface, just a plane (I am limited to one picute per post).

Please, give me feedbacks on my code and suggestions on how to improve it.

Thank you for your support!