resolution——八叉樹解析度，即最小體素的邊長（畫素單位）

getPointIndicesFromNewVoxels() —— 從前一個緩衝區中不存在的所有葉節點獲取索引

switchBuffers()——交換八叉樹快取，但是先前點雲對應的八叉樹結構仍在記憶體中

// pointclouds_octree.cpp: 定義控制檯應用程式的入口點。
//

#include "stdafx.h"
#include<iostream>
#include<fstream>
#include<opencv.hpp>
#include<pcl/io/pcd_io.h>
#include<pcl/point_types.h>
#include<pcl/octree/octree.h>

using namespace std;
using namespace cv;

int main()
{
	fstream file_name1, file_name2;
	ofstream differ;
	differ.open("differ.txt");
	file_name1.open("Result3D_end42.txt");
	file_name2.open("Result3D_end52.txt");

	pcl::PointCloud<pcl::PointXYZ> cloudA;
	pcl::PointCloud<pcl::PointXYZ> cloudB;
	
	cloudA.width = 230088;
	cloudA.height = 1;
	cloudA.is_dense = false;
	cloudA.points.resize (cloudA.width*cloudA.height);

	cloudB.width = 250025;
	cloudB.height = 1;
	cloudB.is_dense = false;  //點雲密度，非密集型
	cloudB.points.resize (cloudB.width*cloudB.height);

	Vec3d dd;
	for (size_t i = 0; i < cloudA.points.size(); i++) {
		file_name1 >> dd[0];
		file_name1 >> dd[1];
		file_name1 >> dd[2];

		cloudA.points[i].x = (double)dd[0];
		cloudA.points[i].y = (double)dd[1];
		cloudA.points[i].z = (double)dd[2];
	}
	for (size_t j = 0; j < cloudB.points.size(); j++) {
		file_name2 >> dd[0];
		file_name2 >> dd[1];
		file_name2 >> dd[2];

		cloudB.points[j].x = (double)dd[0];
		cloudB.points[j].y = (double)dd[1];
		cloudB.points[j].z = (double)dd[2];
	}
	
	float resolution = 10.0f; //八叉樹解析度，即最小體素的邊長
	//pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree(resolution);  //初始化octree
	pcl::octree::OctreePointCloudChangeDetector<pcl::PointXYZ>octree(resolution);  //初始化空間變化檢測物件
	
    //新增cloudA到八叉樹中
	octree.setInputCloud(cloudA.makeShared());
	octree.addPointsFromInputCloud();
	
	octree.switchBuffers(); //交換八叉樹快取，但是coludA對應的八叉樹結構仍在記憶體中

	//新增cloudB到八叉樹中
	octree.setInputCloud(cloudB.makeShared());
	octree.addPointsFromInputCloud();

	vector<int>newPointIdxVector;   //儲存新加入點索引的向量
	octree.getPointIndicesFromNewVoxels(newPointIdxVector);

	for (size_t i = 0; i < newPointIdxVector.size(); i++) {
		differ << cloudB.points[newPointIdxVector[i]].x << " " << cloudB.points[newPointIdxVector[i]].y << " "
			<< cloudB.points[newPointIdxVector[i]].z <<" "<< endl;
	}
	file_name1, file_name2, differ.close();
    return 0;
}

 

目前還不知道如何利用PCL求取兩片點雲的重疊區域！！！

實現效果在Geomagic下的展示如下：

附：
銀色是CloudB中的點，黑色是CloudA中的點，綠色點是檢測出來B中新增加不屬於A的結果點。