我在一个代码中遇到了同样的问题，我用这种方法解决了：

import numpy as np
from scipy.interpolate import griddata
import matplotlib.pylab as plt
from matplotlib import cm

N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]

nx = 10*int(np.sqrt(N))
xg = np.linspace(X.min(), X.max(), nx)
yg = np.linspace(Y.min(), Y.max(), nx)
xgrid, ygrid = np.meshgrid(xg, yg)
ctr_f = griddata((X, Y), Z, (xgrid, ygrid), method='linear')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1) 
ax.contourf(xgrid, ygrid, ctr_f, cmap=cm.coolwarm)

plt.show()

可以使用散点图显示z颜色在x-y轴上的投影。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
# fig = plt.figure()
# ax = fig.add_subplot(projection='3d')
# surf = ax.plot_trisurf(X, Y, Z, cmap=cm.coolwarm, vmin=np.nanmin(Z), vmax=np.nanmax(Z))

fig = plt.figure()

cmap = cm.get_cmap('coolwarm')
color = cmap(Z)[..., :3]

plt.scatter(X,Y,c=color)

plt.show()

因为你似乎有一个中空的3D形状，你可以把投影分成两部分，就像你把形状分成两部分一样。

fig = plt.figure()
plt.subplot(121)
plt.scatter(X[Z<0.5],Y[Z<0.5],c=color[Z<0.5])
plt.title('down part')
plt.subplot(122)
plt.scatter(X[Z>=0.5],Y[Z>=0.5],c=color[Z>+0.5])
plt.title('top part')
plt.show()