PCL1.8.1 Feature - 點的法向量

每一個點計算法向量 

http://pointclouds.org/documentation/tutorials/how_features_work.php#how-3d-features-work

給定輸入點雲，爲每一個點雲中的點估計法向量，估計法向量須要指定每一個點搜索周邊幾個點範圍做爲計算法向量的單位。

此時計算獲得的法向量個數cloud_normals->points.size () 與點雲個數cloud->points.size ()相等。

#include <pcl/point_types.h>
#include <pcl/features/normal_3d.h>
#include <pcl/io/pcd_io.h>
int main()
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);

  pcl::io::loadPCDFile("table_scene_lms400.pcd", *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 ()
}

指定特定點雲中的點計算法向量

取其中10%的點進行法向量計算：std::vector<int> indices (std::floor (cloud->points.size () / 10));

#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 a set of indices to be used. For simplicity, we're going to be using the first 10% of the points in cloud
  std::vector<int> indices (std::floor (cloud->points.size () / 10));
  for (std::size_t i = 0; i < indices.size (); ++i) indices[i] = i;

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

  // Pass the indices
  boost::shared_ptr<std::vector<int> > indicesptr (new std::vector<int> (indices));
  ne.setIndices (indicesptr);

  // 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 indicesptr->size ()
}