Downsampling 3D Point Clouds with a Voxelized Grid

This tutorial covers the process of downsampling / reducing the number of points in a 3D point cloud through a voxelized grid approach.

Note

This tutorial is applicable for execution 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>/voxel_grid
   

   

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

   #include <pcl/oneapi/filters/voxel_grid.h>
   #include <pcl/io/pcd_io.h>
   #include <pcl/point_types.h>
   #include <pcl/point_cloud.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_( new pcl::PointCloud<pcl::PointXYZ>() );
     int result = pcl::io::loadPCDFile("table_scene_lms400.pcd", *cloud_);
     if (result != 0)
     {
       pcl::console::print_info ("Load pcd file failed.\n");
       return result;
     }
   
     // Prepare Point Cloud Memory (output)
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered_( new pcl::PointCloud<pcl::PointXYZ>() );
   
     // GPU calculate
     pcl::oneapi::VoxelGrid<pcl::PointXYZ> vg_oneapi;
     vg_oneapi.setInputCloud(cloud_);
     float leafsize= 0.005f;
     vg_oneapi.setLeafSize (leafsize, leafsize, leafsize);
     vg_oneapi.filter(*cloud_filtered_);
   
     // print log
     std::cout << "[oneapi voxel grid] PointCloud before filtering: " << cloud_->size() << std::endl;
     std::cout << "[oneapi voxel grid] PointCloud after filtering: " << cloud_filtered_->size() << std::endl;
   }
   

   

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_voxel_grid
   

   

7. Expected results example:

   [oneapi voxel grid] PointCloud before filtering: 460400
   [oneapi voxel grid] PointCloud after filtering: 141525
   

   

Code Explanation

Now, let’s explain the code in detail.

First, load the example PCD into a PointCloud<PointXYZ>.

  // Read Point Cloud
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_( new pcl::PointCloud<pcl::PointXYZ>() );
  int result = pcl::io::loadPCDFile("table_scene_lms400.pcd", *cloud_);
  if (result != 0)
  {
    pcl::console::print_info ("Load pcd file failed.\n");
    return result;
  }

Then prepare output buffer for filtered result.

  // Prepare Point Cloud Memory (output)
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered_( new pcl::PointCloud<pcl::PointXYZ>() );

Next, starts to compute the result.

  // GPU calculate
  pcl::oneapi::VoxelGrid<pcl::PointXYZ> vg_oneapi;
  vg_oneapi.setInputCloud(cloud_);
  float leafsize= 0.005f;
  vg_oneapi.setLeafSize (leafsize, leafsize, leafsize);
  vg_oneapi.filter(*cloud_filtered_);

Result(output log).

  // print log
  std::cout << "[oneapi voxel grid] PointCloud before filtering: " << cloud_->size() << std::endl;
  std::cout << "[oneapi voxel grid] PointCloud after filtering: " << cloud_filtered_->size() << std::endl;