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I have a point cloud 

pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGBA>);

that I want to copy into 

pcl::PointCloud<pcl::PointXYZRGBA>::Ptr finalcloud (new pcl::PointCloud<pcl::PointXYZRGBA>);

while filtering based on some inliers calculated using ransac. 

std::vector<int> inliers;

I am currently doing this as 

pcl::copyPointCloud<pcl::PointXYZRGBA>(*cloud, inliers, *finalcloud);

Problem:

Since I want to find the normals for this cloud, I need the organization to be maintained. The copyPointCloud function makes the new point cloud height = 1 (see line 188 of PCL io.hpp ).

Has anyone been able to find normals after performing ransac on a pcl?

user1349663
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  • I'm not sure, but not all procedures (for normal estimation) need organized point clouds. It may work http://www.pointclouds.org/documentation/tutorials/normal_estimation.php#normal-estimation – Kornel Feb 12 '15 at 12:09
  • @Kornel This was the tutorial I used and the setInputCloud() function needs an organised point cloud. – user1349663 Feb 12 '15 at 17:19
  • are you sure? [here](http://docs.pointclouds.org/trunk/features_2include_2pcl_2features_2normal__3d_8h_source.html#l00286) isn't any restriction for the ordered/unordered nature. Only the OMP version needs to have organized clouds. – Kornel Feb 13 '15 at 12:44

1 Answers1

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I think this answer is too late and API might changes from 2015.. but I'll answer it.

The normal estimation will work with both organized cloud and unorganized cloud.

Unorganized Cloud

I'm copying the code from http://pointclouds.org/documentation/tutorials/normal_estimation.php In this code, the KdTree will be used to estimate the neighbours.

#include <pcl/point_types.h>
#include <pcl/features/normal_3d.h>

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

  ... read, pass in or create a point cloud ...

  // Create the normal estimation class, and pass the input dataset to it
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
  ne.setInputCloud (cloud);

  // Create an empty kdtree representation, and pass it to the normal estimation object.
  // Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
  pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
  ne.setSearchMethod (tree);

  // Output datasets
  pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);

  // Use all neighbors in a sphere of radius 3cm
  ne.setRadiusSearch (0.03);

  // Compute the features
  ne.compute (*cloud_normals);

  // cloud_normals->points.size () should have the same size as the input cloud->points.size ()*
}

Organized Cloud

I am copying the code from http://www.pointclouds.org/documentation/tutorials/normal_estimation_using_integral_images.php#normal-estimation-using-integral-images

// load point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile ("table_scene_mug_stereo_textured.pcd", *cloud);

// estimate normals
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);

pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setNormalEstimationMethod (ne.AVERAGE_3D_GRADIENT);
ne.setMaxDepthChangeFactor(0.02f);
ne.setNormalSmoothingSize(10.0f);
ne.setInputCloud(cloud);
ne.compute(*normals);
Ardiya
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