Add some documentation (#1199)

* Add some documentation

* add image

doxygen/HowToExamples.dox

@@ -184,6 +184,78 @@ rotationXf = mrmeshpy.AffineXf3f.linear( mrmeshpy.Matrix3f.rotation( plusZ, 3.14
 mesh.transform(rotationXf)
 \endcode
 
+Simple triangulation
+\code
+from meshlib import mrmeshpy as mm
+from meshlib import mrmeshnumpy as mn
+import numpy as np
+
+u, v = np.mgrid[0:2 * np.pi:100j, 0:np.pi:100j]
+x = np.cos(u) * np.sin(v)
+y = np.sin(u) * np.sin(v)
+z = np.cos(v)
+
+# Prepare for MeshLib PointCloud
+verts = np.stack((x.flatten(),y.flatten(),z.flatten()),axis=-1).reshape(-1,3)
+# Create MeshLib PointCloud from np ndarray
+pc = mn.pointCloudFromPoints(verts)
+# Remove duplicate points
+pc.validPoints = mm.pointUniformSampling(pc,1e-3)
+pc.invalidateCaches()
+
+# Triangulate it
+triangulatedPC = mm.triangulatePointCloud(pc)
+
+# Fix possible issues
+triangulatedPC = mm.offsetMesh(triangulatedPC, 0.0)
+\endcode
+
+Tirangulation of regular numpy grid
+\code
+from meshlib import mrmeshpy as mm
+from meshlib import mrmeshnumpy as mn
+import numpy as np
+
+u, v = np.mgrid[0:2 * np.pi:50j, 0:np.pi:100j]
+x = np.cos(u) * np.sin(v)
+y = np.sin(u) * np.sin(v)
+z = np.cos(v)
+
+mesh = mn.meshFromUVPoints(x,y,z)
+\endcode
+
+Using plotly to visualize mesh
+\code
+from meshlib import mrmeshpy as mm
+from meshlib import mrmeshnumpy as mn
+import numpy as np
+import plotly.graph_objects as go
+
+# load mesh
+mesh = mm.loadMesh(mm.Path("mesh.stl"))
+# extract numpy arrays
+verts = mn.getNumpyVerts(mesh)
+faces = mn.getNumpyFaces(mesh.topology)
+
+# prepare data for plotly
+vertsT = np.transpose(verts)
+facesT = np.transpose(faces)
+
+# draw
+fig = go.Figure(data=[
+    go.Mesh3d(
+        x = vertsT[0],
+        y = vertsT[1],
+        z = vertsT[2],
+        i = facesT[0],
+        j = facesT[1],
+        k = facesT[2]
+    )
+])
+
+fig.show()
+\endcode
+\image html plotly_visualization.png "Plotly visualization" width = 300cm
 
 \sa \ref EmbendedPythonOverview
 */