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三维重建工具pcyly教程——如何使用 KdTree 进行搜索

作者:互联网

本教程代码开源:GitHub 欢迎fork

文章目录

前言

在本教程中,将介绍如何使用 KdTree 查找特定点或位置的 K 个最近邻,还将介绍如何查找用户指定的某个半径内的所有邻居(在这种情况下是随机的) 。

理论入门

kd 树或 k 维树是计算机科学中使用的一种数据结构,用于在具有 k 维的空间中组织一定数量的点。它是一个二叉搜索树,对其施加了其他约束。Kd 树对于范围和最近邻搜索非常有用。出于我们的目的,我们通常只会处理三维的点云,因此我们所有的 kd 树都是三维的。kd 树的每一层使用垂直于相应轴的超平面沿特定维度拆分所有子节点。在树的根部,所有子节点都将根据第一维进行拆分(即,如果第一维坐标小于根,它将在左子树中,如果大于根,则显然将在左子树中右子树)。树中的每一层都在下一个维度上进行划分,一旦所有其他维度都用尽,则返回到第一个维度。构建 kd 树的最有效方法是使用像 Quick Sort 那样的分区方法,将中点放在根处,将一维值较小的所有内容放在左侧,右侧较大。然后在左子树和右子树上重复此过程,直到要分区的最后一棵树仅由一个元素组成。

来自[维基百科]请添加图片描述

这是一个二维 kd 树的例子:
请添加图片描述
这是最近邻搜索如何工作的演示。

参考:https://pcl.readthedocs.io/projects/tutorials/en/latest/kdtree_search.html#kdtree-search

pclpy代码

kdTreeDemo.py

import pclpy
from pclpy import pcl
import numpy as np

if __name__ == '__main__':
    # 生成点云数据
    cloud_size = 100
    a = np.random.ranf(cloud_size * 3).reshape(-1, 3) * 1024
    cloud = pcl.PointCloud.PointXYZ.from_array(a)

    kdtree = pcl.kdtree.KdTreeFLANN.PointXYZ()
    kdtree.setInputCloud(cloud)
    searchPoint = pcl.point_types.PointXYZ()
    searchPoint.x = np.random.ranf(1) * 1024
    searchPoint.y = np.random.ranf(1) * 1024
    searchPoint.z = np.random.ranf(1) * 1024
    # k最近邻搜索
    k = 8
    pointIdxNKNSearch = pclpy.pcl.vectors.Int([0] * k)
    pointNKNSquaredDistance = pclpy.pcl.vectors.Float([0] * k)
    print('K nearest neighbor search at (', searchPoint.x,
          '', searchPoint.y,
          '', searchPoint.z,
          ') with k =', k, '\n')
    if kdtree.nearestKSearch(searchPoint, k, pointIdxNKNSearch, pointNKNSquaredDistance) > 0:
        for i in range(len(pointIdxNKNSearch)):
            print("  ", cloud.x[pointIdxNKNSearch[i]],
                  " ", cloud.y[pointIdxNKNSearch[i]],
                  " ", cloud.z[pointIdxNKNSearch[i]],
                  " (squared distance: ", pointNKNSquaredDistance[i], ")", "\n")

    # 使用半径最近邻搜索
    pointIdxNKNSearch = pclpy.pcl.vectors.Int()
    pointNKNSquaredDistance = pclpy.pcl.vectors.Float()

    radius = np.random.ranf(1) * 256.0
    print("Neighbors within radius search at (", searchPoint.x,
          " ", searchPoint.y, " ", searchPoint.z, ") with radius=",
          radius, '\n')
    if kdtree.radiusSearch(searchPoint, radius, pointIdxNKNSearch, pointNKNSquaredDistance) > 0:
        for i in range(len(pointIdxNKNSearch)):
            print("  ", cloud.x[pointIdxNKNSearch[i]],
                  " ", cloud.y[pointIdxNKNSearch[i]],
                  " ", cloud.z[pointIdxNKNSearch[i]],
                  " (squared distance: ", pointNKNSquaredDistance[i], ")", "\n")

说明

以下代码首先利用Numpy使用随机数据创建和填充 PointCloud。

# 生成点云数据
cloud_size = 100
a = np.random.ranf(cloud_size * 3).reshape(-1, 3) * 1024
cloud = pcl.PointCloud.PointXYZ.from_array(a)

下一段代码创建我们的 kdtree 对象并将我们随机创建的云设置为输入。然后我们创建一个“searchPoint”,它被分配了随机坐标。

kdtree = pcl.kdtree.KdTreeFLANN.PointXYZ()
kdtree.setInputCloud(cloud)
searchPoint = pcl.point_types.PointXYZ()
searchPoint.x = np.random.ranf(1) * 1024
searchPoint.y = np.random.ranf(1) * 1024
searchPoint.z = np.random.ranf(1) * 1024

现在我们创建一个整数(并将其设置为 10)和两个向量,用于存储来自搜索的 K 个最近邻。

k = 8
pointIdxNKNSearch = pclpy.pcl.vectors.Int([0] * k)
pointNKNSquaredDistance = pclpy.pcl.vectors.Float([0] * k)
print('K nearest neighbor search at (', searchPoint.x,
      '', searchPoint.y,
      '', searchPoint.z,
      ') with k =', k, '\n')

假设我们的 KdTree 返回 0 个以上的最近邻,它然后打印出所有 10 个最近邻的位置到我们的随机“searchPoint”,这些位置已经存储在我们之前创建的向量中。

if kdtree.nearestKSearch(searchPoint, k, pointIdxNKNSearch, pointNKNSquaredDistance) > 0:
    for i in range(len(pointIdxNKNSearch)):
        print("  ", cloud.x[pointIdxNKNSearch[i]],
        " ", cloud.y[pointIdxNKNSearch[i]],
        " ", cloud.z[pointIdxNKNSearch[i]],
        " (squared distance: ", pointNKNSquaredDistance[i], ")", "\n")

下面再演示一下使用半径最近邻搜索,在某个(随机生成的)半径内找到给定“searchPoint”的所有邻居。再次创建了 2 个向量来存储有关我们邻居的信息。

# 使用半径最近邻搜索
pointIdxNKNSearch = pclpy.pcl.vectors.Int()
pointNKNSquaredDistance = pclpy.pcl.vectors.Float()

radius = np.random.ranf(1) * 256.0
print("Neighbors within radius search at (", searchPoint.x,
          " ", searchPoint.y, " ", searchPoint.z, ") with radius=",
          radius, '\n')

同样,和以前一样,如果我们的 KdTree 在指定半径内返回 0 个以上的邻居,它会打印出这些点的坐标,这些点已经存储在我们的向量中。

if kdtree.radiusSearch(searchPoint, radius, pointIdxNKNSearch, pointNKNSquaredDistance) > 0:
        for i in range(len(pointIdxNKNSearch)):
            print("  ", cloud.x[pointIdxNKNSearch[i]],
                  " ", cloud.y[pointIdxNKNSearch[i]],
                  " ", cloud.z[pointIdxNKNSearch[i]],
                  " (squared distance: ", pointNKNSquaredDistance[i], ")", "\n")

运行

运行kdTreeDemo.py,即可

运行结果:

K nearest neighbor search at ( 737.6050415039062 750.3650512695312 411.2821960449219 ) with k = 8

753.9883 643.7369 456.1752 (squared distance: 13653.359375 )

828.60803 626.9191 501.09518 (squared distance: 31586.8125 )

760.72687 627.8939 539.5448 (squared distance: 31985.091796875 )

810.8796 972.1281 278.54584 (squared distance: 72166.953125 )

598.6487 507.64853 444.17035 (squared distance: 79301.8125 )

649.69885 946.6329 597.18005 (squared distance: 80806.5625 )

476.53268 646.1927 467.837 (squared distance: 82209.1015625 )

878.47424 922.55475 621.521 (squared distance: 93693.7734375 )

Neighbors within radius search at ( 737.6050415039062 750.3650512695312 411.2821960449219 ) with radius= [203.92877983]

753.9883 643.7369 456.1752 (squared distance: 13653.359375 )

828.60803 626.9191 501.09518 (squared distance: 31586.8125 )

760.72687 627.8939 539.5448 (squared distance: 31985.091796875 )

注意:由于我们的数据是随生成的,每次结果都不一样,甚至有时候可能KdTree 返回 0 最近邻,这时候就没有输出了。

标签:pointIdxNKNSearch,KdTree,squared,searchPoint,三维重建,pcl,pcyly,cloud,distance
来源: https://blog.csdn.net/weixin_44456692/article/details/120391246