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Plane Model Segmentation

In this tutorial we will learn how to do a simple plane segmentation from a set of points to find all the points within a point cloud that support a plane model.

Note

This tutorial can be run both inside and outside a Docker* image. We assume that the pcl-oneapi-tutorial Deb package has been 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>/segmentation
   

   

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

   #include <pcl/oneapi/segmentation/segmentation.h>
   #include <pcl/io/pcd_io.h>
   #include <pcl/point_types.h>
   
   int main (int argc, char **argv)
   {
       std::cout << "Running on device: " << dpct::get_default_queue().get_device().get_info<sycl::info::device::name>() << "\n";
   
       //Read Point Cloud
       pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_input (new pcl::PointCloud<pcl::PointXYZ> ());
   
       //Load a standard PCD file from disk
       int result = pcl::io::loadPCDFile("test59.pcd", *cloud_input);
       if (result != 0)
       {
           pcl::console::print_info ("Load pcd file failed.\n");
           return result;
       }
   
       //Create the oneapi_segmentation object
       pcl::oneapi::SACSegmentation seg;
   
       //Configure oneapi_segmentation class 
       seg.setInputCloud(cloud_input);
       seg.setProbability(0.99);
       seg.setMaxIterations(50);
       seg.setDistanceThreshold(0.01);
       //Optional  
       seg.setOptimizeCoefficients(true);
       //Set algorithm method and model type 
       seg.setMethodType(pcl::oneapi::SAC_RANSAC);
       seg.setModelType (pcl::oneapi::SACMODEL_PLANE);
   
       //Out parameter declaration for getting inliers and model coefficients  
       pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
       double coeffs[4]={0,0,0,0};
   
       //Getting inliers and model coefficients
       seg.segment(*inliers, coeffs);
   
       std::cout << "input cloud size   : " << seg.getCloudSize() << std::endl;
       std::cout << "inliers size       : " << seg.getInliersSize() << std::endl;
       std::cout << "model coefficients : " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << 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_segmentation
   

   

7. Expected results example:

   input cloud size   : 307200
   inliers size       : 25332
   model coefficients : -0.176599, -1.87228, -1.08408, 1
   

   

Code Explanation

Load the test data from GitHub* into a PointCloud<PointXYZ>.

    //Read Point Cloud
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_input (new pcl::PointCloud<pcl::PointXYZ> ());

Create the oneapi_segmentation object.

    pcl::oneapi::SACSegmentation seg;

Configure the oneapi_segmentation class.

    seg.setInputCloud(cloud_input);
    seg.setProbability(0.99);
    seg.setMaxIterations(50);
    seg.setDistanceThreshold(0.01);
    //Optional  
    seg.setOptimizeCoefficients(true);
    //Set algorithm method and model type 
    seg.setMethodType(pcl::oneapi::SAC_RANSAC);
    seg.setModelType (pcl::oneapi::SACMODEL_PLANE);

Set to true if a coefficient refinement is required.

    seg.setOptimizeCoefficients(true);

Set the algorithm method and model type.

    seg.setMethodType(pcl::oneapi::SAC_RANSAC);
    seg.setModelType (pcl::oneapi::SACMODEL_PLANE);

Declare output parameters for getting inliers and model coefficients.

    pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
    double coeffs[4]={0,0,0,0};

Get inliers and model coefficients by calling the segment() API.

    seg.segment(*inliers, coeffs);

Result (output log):

    std::cout << "input cloud size   : " << seg.getCloudSize() << std::endl;
    std::cout << "inliers size       : " << seg.getInliersSize() << std::endl;
    std::cout << "model coefficients : " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;