最近在看PCL的教程，發現對點雲中具體資料點的訪問也有好幾種方式，看著看著就會混淆了，所以，現將每種方式記錄下來，做個對比，方便隨時複習，溫故知新。

一、

第一種是在看《How to create a range image from a point cloud》教程時看到的，程式碼如下，這種方式是一種vector的賦值方式，首先將point資料push_back到pcl::PointXYZ型別的模板中，然後再用無序點雲的方式重新組織點雲資料。

  pcl::PointCloud<pcl::PointXYZ> pointCloud;
  
  // Generate the data
 

  for (float y=-0.5f; y<=0.5f; y+=0.01f) {
    for (float z=-0.5f; z<=0.5f; z+=0.01f) {
      pcl::PointXYZ point;
      point.x = 2.0f - y;
      point.y = y;
      point.z = z;
      pointCloud.points.push_back(point);
    }
  }
  pointCloud.width = (uint32_t) pointCloud.points.size();
  pointCloud.
 
height = 1;

出處《Filtering a PointCloud using a PassThrough filter》，指標型類模板，採用“->points[i]”方式賦值。

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // Fill in the cloud data
 

  cloud->width  = 5;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }

  std::cerr << "Cloud before filtering: " << std::endl;
  for (size_t i = 0; i < cloud->points.size (); ++i)
    std::cerr << "    " << cloud->points[i].x << " " 
                        << cloud->points[i].y << " " 
                        << cloud->points[i].z << std::endl;