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PolyData.delaunay_2d
Apply a 2D Delaunay filter along the best fitting plane.
该函数仅仅用于从点云数据生成2D平面，而非曲面
PolyData.delaunay_2d(tol=1e-05, alpha=0.0, offset=1.0, bound=False, inplace=False, edge_source=None, progress_bar=False)
delaunay_2d — PyVista 0.36.1 documentation
例：
import pyvista
# Polygon(center=(0.0, 0.0, 0.0), radius=1, normal=(0, 0, 1), n_sides=6)
points = pyvista.Polygon(n_sides=30).points
circle = pyvista.PolyData(points)
circle.plot(show_edges=True, point_size=15)
filled_circle = circle.delaunay_2d()
filled_circle.plot(show_edges=True, line_width=5)
点云数据：
平面网格：
PolyData.delaunay_3d
PolyData.delaunay_3d(alpha=0, tol=0.001, offset=2.5, progress_bar=False)
delaunay_3d — PyVista 0.36.1 documentation
该函数实际上是从面或点云数据生成四面体网格，而非三角形面网格！
例
import pyvista
sphere = pyvista.Sphere(theta_resolution=5, phi_resolution=5)
grid = sphere.delaunay_3d()
edges = grid.extract_all_edges()
edges.plot(line_width=5, color='k')
PolyDataFilters.reconstruct_surface
PolyDataFilters.reconstruct_surface(nbr_sz=None, sample_spacing=None, progress_bar=False)
reconstruct_surface — PyVista 0.36.1 documentation
这是真正的从点云数据生成面的函数，采用隐式面重建方法（A signed measure of the distance to the surface is computed and sampled on a regular grid. The grid can then be contoured at zero to extract the surface. ）然而，真正的大量点存在时（测试案例：26000点，等10分钟未成功，MATLAB几秒钟完成），效率堪忧。
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