I would like to create a Surface or Mesh from a cloud of points that are at the contour of the object to be meshed.

What I have is a set of points with x and y for each z, similar to a topographic map, however the x and y are not unique like in those type of maps.

I think it will be easier with an example and an image:

Imagine you have a set of point like above.

How could I plot the shape of that cloud instead of the points?

I am open to any other 3D representation or approach in order to represent it in a cleaner way.

The points in the image are from two spheres: the shown points are the countor of one sphere and the intersection of the second one contour inside the first sphere. Use the following code to generate it:

import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px


def equation(x, y, z):
    a1 = 0; b1 = 0; c1 = 0; r1 = 5
    sphere1_inside = (x - a1) ** 2 + (y - b1) ** 2  + (z - c1) ** 2  <= r1 ** 2 
    sphere1_inperimeter = abs(r1 ** 2 - ((x - a1) ** 2 + (y - b1) ** 2  + (z - c1) ** 2 )) < 0.05
    a2 = 1; b2 = 1; c2 = -1; r2 = 4
    sphere2_inside = (x - a2) ** 2 + (y - b2) ** 2  + (z - c2) ** 2  <= r2 ** 2 
    sphere2_inperimeter = abs(r2 ** 2 - ((x - a2) ** 2 + (y - b2) ** 2  + (z - c2) ** 2 )) < 0.05

    return (sphere1_inperimeter and not sphere2_inside) or (sphere1_inside and sphere2_inperimeter)

data = {
    'x': [],
    'y': [],
    'z': [],
    'size': []
    }

for z in np.linspace(-5, 5, num=20):
    for y in np.linspace(-5, 5, num=200):
        for x in np.linspace(-5, 5, num=200):
            if(equation(x, y, z)):  
                data['x'].append(x)
                data['y'].append(y)
                data['z'].append(z)
                data['size'].append(5)

df = pd.DataFrame(data)

fig = px.scatter_3d(df,
                    x='x',
                    y='y',
                    z='z',
                    size='size',
                    size_max=df['size'].max(),
                    title='plotly',
                    opacity=0.8,
                    color_continuous_scale='viridis',
                    )

fig.show()