解决方案是使用分层聚类算法将大型点云划分为几个较小的子集，在每个子集上运行Alphashape算法并将结果合并。该方法可以大大减少内存占用和运行时间，避免出现意外的多边形产生。

代码示例：

import numpy as np
from sklearn.cluster import MiniBatchKMeans
from scipy.spatial import ConvexHull, Delaunay

def alphashape(points, alpha):
    tri = Delaunay(points)
    edges = set()
    edge_points = []
    for i, j, k in tri.vertices:
        if i < j:
            edges.add((i, j))
            edge_points.append(points[[i, j]])
        if j < k:
            edges.add((j, k))
            edge_points.append(points[[j, k]])
        if k < i:
            edges.add((k, i))
            edge_points.append(points[[k, i]])
    edge_points = np.array(edge_points)

    radii = np.sum((points[edge_points[:, 0]] - points[edge_points[:, 1]]) ** 2, axis=1)
    too_small = radii < alpha ** 2
    edges = np.array(list(edges))
    edge_points = edge_points[too_small]

    clusters = MiniBatchKMeans(n_clusters=int(len(points) / 500), batch_size=min(len(points) / 5, 10000)).fit_predict(points)
    polygon_points = []
    for c in np.unique(clusters):
        c_points = points[clusters == c]
        c_edge_points = edge_points[(clusters[edges[:, 0]] == c) & (clusters[edges[:, 1]] == c)]
        hull = ConvexHull(c_points)
        for simplex in hull.simplices:
            polygon_points.append(c_points[simplex])
        if c_edge_points.size > 0:
            c_alpha_points = []
            for i, j in c_edge_points:
                if radii[(edges[:, 0] == i) & (edges[:, 1] == j)][0] < alpha ** 2:
                    c_alpha_points.append(np.intersect1d(edge_points[(edges[:, 0] == i) & (edges[:, 1] == j)][0], c_points))
            if len(c_alpha_points) > 0:
                polygon_points.append(np.vstack(c_alpha_points))

    return polygon_points

在这里，MiniBatchKMeans类用于将点云划分为多个子集，而ConvexHull和Delaunay类分别用于计算凸包和三角剖分。edges变量包含所有Delaunay三角形的边缘（去重），而edge_points变量包含所有边缘点。最后，通过合并所有多边形可得到完整的Alpha形状。