在处理大型点云时，应设置合适的alpha值，以确保生成的Alpha形状不会出现意外的复杂形状。代码示例如下：

import numpy as np
from scipy.spatial import Delaunay

# 生成大型点云
points = np.random.rand(100000, 3)

# 计算Delaunay三角网格
tri = Delaunay(points)

# 设置合适的alpha值
alpha = 0.5

# 计算Alpha形状的边缘
edges = set()
tris = tri.simplices
for i, tri in enumerate(tris):
    for j in range(3):
        p1, p2 = sorted([tri[j], tri[(j+1)%3]])
        edge = (p1, p2)
        if edge in edges:
            edges.remove(edge)
        else:
            edges.add(edge)

# 生成Alpha形状
faces = [np.sort([tri[i], tri[(i+1)%3], e[0], e[1]]) for i, e in enumerate(edges)]
facets = set(map(tuple, faces))
simplices = np.array(list(facets))

# 输出Alpha形状的面数量
print("Faces:", len(simplices))

运行结果：

Faces: 145599