基于八叉树的3d点体素化以及可视化python代码

以下是一个简单的基于八叉树的3D点体素化和可视化的Python代码示例：

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt


class Octree:
    def __init__(self, center, half_size, depth=0, max_depth=5):
        self.center = center
        self.half_size = half_size
        self.depth = depth
        self.max_depth = max_depth
        self.children = [None] * 8

    def insert(self, point):
        if self.depth == self.max_depth:
            return

        index = 0
        if point[0] > self.center[0]:
            index |= 4
        if point[1] > self.center[1]:
            index |= 2
        if point[2] > self.center[2]:
            index |= 1

        if self.children[index] is None:
            half_size = self.half_size / 2
            child_center = np.zeros(3)
            if index & 4:
                child_center[0] = self.center[0] + half_size
            else:
                child_center[0] = self.center[0] - half_size
            if index & 2:
                child_center[1] = self.center[1] + half_size
            else:
                child_center[1] = self.center[1] - half_size
            if index & 1:
                child_center[2] = self.center[2] + half_size
            else:
                child_center[2] = self.center[2] - half_size
            self.children[index] = Octree(child_center, half_size, self.depth + 1, self.max_depth)

        self.children[index].insert(point)

    def visualize(self, ax):
        if self.depth == self.max_depth:
            ax.scatter(self.center[0], self.center[1], self.center[2], color='red')
            return

        for child in self.children:
            if child is not None:
                child.visualize(ax)


def main():
    # Generate random 3D points within a cube
    points = np.random.rand(100, 3)

    # Create octree
    center = np.array([0.5, 0.5, 0.5])
    half_size = 0.5
    octree = Octree(center, half_size, max_depth=4)
    for point in points:
        octree.insert(point)

    # Visualize octree
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    octree.visualize(ax)
    ax.set_xlim([0, 1])
    ax.set_ylim([0, 1])
    ax.set_zlim([0, 1])
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')
    plt.show()


if __name__ == '__main__':
    main()

该代码生成了100个随机3D点，并将它们插入到一个深度为4的八叉树中。然后，使用matplotlib库中的3D散点图将八叉树可视化。在可视化中，每个叶子节点用红色点表示。