Meshing on simple curve uniform plan using vedo

Shared the ply file above. please can you see add some feature or can check what changes should be done to mesh for this kind of 3d objects. I tried but I am not getting an perfect mesh. as the plane is curve and uniform so please check it plyfile

from vedo import *

plt = Plotter(N=3, axes=dict(zxGrid=True, xyGrid=False))

vpts = load("/home/anveshak-nikhil/Only_codes_my/3march_generate_pc/23_march_bound.ply").clean()
# vpts = load("data/Final_sample_2.ply").clean()
vpts.ps(5).renderPointsAsSpheres().c('k3').alpha(0.6)

# this block removes the vertical lines
print("before", vpts.N())
newpts =[]
for p in vpts.points():
    cps = vpts.closestPoint(p, N=2)
    for cp in cps:
        if mag(cp-p)<0.00000001: # it's the same point p
            continue
        elif mag(cp-p)>0.00001:  # p is "isolated"
            newpts.append(p)
vpts = Points(newpts).clean()
print("after", vpts.N())

vpts_orig = vpts.clone() # make a copy
plt.show(vpts_orig, "original pcloud", at=0)

print("Nr of points before cleaning nr. points:", vpts.N())
vpts.clean(tol=0.0005)  # impose a min distance among points
print("after cleaning nr. points:", vpts.N())

print("smoothing cloud")
vpts.smoothMLS2D(f=0.15)  # smooth cloud by fitting planes recursively
plt.show(vpts, "cloud smoothed", at=1)

# reconstructed surface from point cloud
reco = recoSurface(vpts.points(), dims=[250,36,250], radius=0.002).extractLargestRegion().clean()
reco.subdivide().smoothLaplacian().fillHoles().computeNormals()
# reco.write("result.ply")

plt.show(reco, vpts, "reconstruction", at=2, zoom=1.2, interactive=True)

[ghost]

Thank you so much for your reply… it means me a lot… i apprecticate you…is there not any other solution for this for 3d model…

[marcomusy]

you may play with tetrahedral meshes but it’s probably not a good solution anyway: (you won’t be able to close holes because the mesh is not manifold)

from vedo import *

plt = Plotter(N=2, axes=dict(zxGrid=True, xyGrid=False))

vpts = load("data/Sample_demo.ply").clean()
vpts.ps(5).c('k3').alpha(0.6)
vpts_orig = vpts.clone() # make a copy

print("Nr of points before cleaning nr. points:", vpts.N())
vpts.clean(tol=0.0005)  # impose a min distance among points
print("after cleaning nr. points:", vpts.N())

# reconstructed surface from point cloud
dela = delaunay3D(vpts, alphaPar=0.0014) # tetrahedral mesh
dela = geometry(dela.inputdata()) # outer Mesh

plt.show(vpts_orig, "original pcloud", at=0)
plt.show(vpts, dela, "reconstruction", at=1, zoom=1.2, interactive=1)