Estimating Surface Normals in a PointCloud

In this tutorial, we will learn how to obtain the surface normals of each point in the cloud.

Note

This tutorial is applicable for execution for both within inside and outside a Docker* image. It assumes that the pcl-oneapi-tutorials Deb package is installed, and the user has copied the tutorial directory from /opt/intel/pcl/oneapi/tutorials/ to a user-writable directory.

1. Prepare the environment:

   cd <path-to-oneapi-tutorials>/normal_estimation
   

   

2. oneapi_normal_estimation.cpp should be in the directory with following content:

   #include <pcl/io/pcd_io.h>
   #include <pcl/oneapi/features/normal_3d.h>
   #include <pcl/oneapi/kdtree/kdtree_flann.h>
   #include <pcl/oneapi/point_cloud.h>
   
   int main (int argc, char** argv)
   {
     int k = 10;
     float radius = 0.01;
   
     std::cout << "Running on device: " << dpct::get_default_queue().get_device().get_info<sycl::info::device::name>() << "\n";
   
     // load point cloud
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr( new pcl::PointCloud<pcl::PointXYZ>() );
   
     int result = pcl::io::loadPCDFile("bun0.pcd", *cloud_ptr);
     if (result != 0)
     {
       pcl::console::print_info ("Load pcd file failed.\n");
       return result;
     }
   
     // estimate normals with knn search
     pcl::oneapi::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
     ne.setSearchMethod (pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>::Ptr (new pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>));
     ne.setInputCloud(cloud_ptr);
     ne.setKSearch(k);
   
     // save normal estimation to CPU memory point cloud
     pcl::PointCloud<pcl::Normal>::Ptr normals_knn(new pcl::PointCloud<pcl::Normal>);
     ne.compute(*normals_knn);
   
     std::cout << "normals_knn.size (): " << normals_knn->size () << std::endl;
   
     // estimate normals with radius search
     ne.setSearchMethod (pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>::Ptr (new pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>));
     ne.setInputCloud(cloud_ptr);
     ne.setRadiusSearch(radius);
     ne.setKSearch(0);
   
     // save normal estimation output to device shared memory point cloud
     pcl::oneapi::PointCloudDev<pcl::Normal>::Ptr normals_radius(new pcl::oneapi::PointCloudDev<pcl::Normal>) ;
     ne.compute(*normals_radius);
   
     std::cout << "normals_radius.size (): " << normals_radius->size () << std::endl;
   
     return 0;
   }
   

   

3. Source the Intel® oneAPI Base Toolkit environment:

   source /opt/intel/oneapi/setvars.sh
   

   

4. (Optional) Setup proxy setting to download test data:

   export http_proxy="http://<http_proxy>:port"
   export https_proxy="http://<https_proxy>:port"
   

   

5. Build the code:

   mkdir build && cd build
   cmake ../
   make -j
   

   

6. Run the binary:

   ./oneapi_normal_estimation
   

   

7. Expected results example:

   normals_knn.size (): 397
   normals_radius.size (): 397
   

   

Code Explanation

The example PCD is initially loaded into a PointCloud<PointXYZ>.

  // load point cloud
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr( new pcl::PointCloud<pcl::PointXYZ>() );

  int result = pcl::io::loadPCDFile("bun0.pcd", *cloud_ptr);
  if (result != 0)
  {
    pcl::console::print_info ("Load pcd file failed.\n");
    return result;
  }

This tutorial includes two normal estimation processes: KNN search and Radius search. The initial step involves adjusting the parameters for normal estimation in the KNN search.

  // estimate normals with knn search
  pcl::oneapi::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
  ne.setSearchMethod (pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>::Ptr (new pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>));
  ne.setInputCloud(cloud_ptr);
  ne.setKSearch(k);

Normal estimation is then executed.

  pcl::PointCloud<pcl::Normal>::Ptr normals_knn(new pcl::PointCloud<pcl::Normal>);
  ne.compute(*normals_knn);

The parameters for normal estimation are modified for the radius search.

  // estimate normals with radius search
  ne.setSearchMethod (pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>::Ptr (new pcl::oneapi::KdTreeFLANN<pcl::PointXYZ>));
  ne.setInputCloud(cloud_ptr);
  ne.setRadiusSearch(radius);
  ne.setKSearch(0);

Normal estimation is performed once more.

  // save normal estimation output to device shared memory point cloud
  pcl::oneapi::PointCloudDev<pcl::Normal>::Ptr normals_radius(new pcl::oneapi::PointCloudDev<pcl::Normal>) ;
  ne.compute(*normals_radius);