很难说什么会对你的实际使用情况起作用，但我用两个不匹配的气缸作为例子数据拼凑了一些东西。
这个想法和你所阐述的差不多，只是可能做得更仔细一些。
find the boundary edges of both meshes
use some heuristic to identify edges that should be fused (I assume this is already solved for your use case),
pull out cells in both meshes that contain the respective edge lines, delete these cells (along with edge points) from the original meshes
triangulate these cells using
delaunay_3d()
use some heuristic to discard the "capping" introduced by triangulation; looking at your screenshot what I did will probably work: check the cell normals and discard "too horizontal" cells
merge the two truncated input meshes and the middle triangulated strip
我在代码中留下了一些评论，例如，假设你的网格是三角化的，或者如果不是，它们可以被三角化。如果我们不能对网格进行三角测量，那么
remove_points()
就不会起作用，所以我们必须通过单元提取来获得相同的结果（不是太难，只是比较麻烦）。
该代码首先绘制了输入的网格以显示不匹配的情况，最后绘制了合并后的网格。替换代码0】抱怨网格质量问题，这可能与三角剖分中沿缝的8个较大的三角形有关。这是否会影响到你的真实用例，我无法猜测。
import pyvista as pv
# plotting setup
theme = pv.themes.DocumentTheme()
theme.show_edges = True
# create dummy meshes
mesh1 = pv.Cylinder(resolution=8, center=(0, 0, 0.5), direction=(0, 0, 1), capping=False).triangulate().subdivide(3)
mesh2 = pv.Cylinder(resolution=16, center=(0, 0, -0.5), direction=(0, 0, 1), capping=False).triangulate().subdivide(3)
mesh2.rotate_z(360/32, inplace=True)
# plot them together
plotter = pv.Plotter(theme=theme)
plotter.add_mesh(mesh1, color='pink')
plotter.add_mesh(mesh2, color='cyan')
plotter.show()
edge_kwargs = dict(
manifold_edges=False,
non_manifold_edges=False,
feature_edges=False,
boundary_edges=True,
def tag_and_extract(mesh):
"""The work we have to do for both meshes.
This adds some scalars and extracts cells involved in the common
edge of both meshes. You'll need to tweak the filtering that selects
the appropriate feature edges below, see the "first hack" comment.
This will also modify the input mesh by deleting edge points and
the cells containing them. If you want to preserve your input
mesh, pass in a copy, and use that copy after calling this
function.
# grab interesting edges
# add scalars to keep track of point and cell indices after extraction
mesh.point_data['point_inds'] = range(mesh.n_points)
mesh.cell_data['cell_inds'] = range(mesh.n_cells)
mesh_edges = mesh.extract_feature_edges(**edge_kwargs)
# first hack:
# you'll need some way to locate corresponding pairs of edges; this is specific to your problem
# in this example there's only one pair of edges so we'll clip with two planes
mesh_edges = mesh_edges.clip('z', origin=mesh1.center).clip('-z', origin=mesh2.center)
# extract original cells containing edge lines
mesh_edge_cells = mesh.extract_points(mesh_edges['point_inds'])
# delete edge points along with their containing cells
# the example data is already triangulated, otherwise we'd have to triangulate it
# (or have to do more work)
mesh.remove_points(mesh_edges['point_inds'], inplace=True)
return mesh_edge_cells
mesh1_edge_cells = tag_and_extract(mesh1)
mesh2_edge_cells = tag_and_extract(mesh2)
# triangulate the edge strip
edge_strip = (mesh1_edge_cells + mesh2_edge_cells).delaunay_3d().extract_surface()
# second hack that needs fitting to your problem: remove capping
normals = edge_strip.compute_normals().cell_data['Normals']
horizontals = abs(normals[:, -1]) < 0.9 # has lots of leeway
edge_strip = edge_strip.extract_cells(horizontals).extract_surface()
# merge meshes
merged = mesh1 + edge_strip + mesh2
# plot the result
plotter = pv.Plotter(theme=theme)
plotter.add_mesh(merged)
plotter.show()
Screenshot of the input meshes:
Merged mesh:
希望有更好（和更可靠）的方法，但这是我最好的猜测。
