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Detecting Specific Models and Their Parameters in 3D Point Clouds

In this tutorial we will learn how to detect a plane model in a 3D point cloud.

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

     1/*
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 4 *  Point Cloud Library (PCL) - www.pointclouds.org
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38
39#include <pcl/oneapi/sample_consensus/sac_model_plane.h>
40#include <pcl/oneapi/sample_consensus/ransac.h>
41#include <pcl/io/pcd_io.h>
42#include <pcl/point_types.h>
43#include <pcl/point_cloud.h>
44
45
46int main (int argc, char** argv)
47{
48  std::cout << "Running on device: " << dpct::get_default_queue().get_device().get_info<sycl::info::device::name>() << "\n";
49  // Read Point Cloud
50  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr( new pcl::PointCloud<pcl::PointXYZ>() );
51  pcl::PointCloud<pcl::PointXYZ>::Ptr convex_ptr;
52  int result = pcl::io::loadPCDFile("test59.pcd", *cloud_ptr);
53  if (result != 0)
54  {
55    pcl::console::print_info ("Load pcd file failed.\n");
56    return result;
57  }
58
59  // Prepare Device Point Cloud Memory
60  pcl::oneapi::SampleConsensusModel::PointCloud_xyz cloud_device_xyz;
61  cloud_device_xyz.upload(cloud_ptr->points);
62  pcl::oneapi::SampleConsensusModel::PointCloud & cloud_device = (pcl::oneapi::SampleConsensusModel::PointCloud &)cloud_device_xyz;
63
64  // Algorithm tests
65  typename pcl::oneapi::SampleConsensusModelPlane::Ptr sac_model (new pcl::oneapi::SampleConsensusModelPlane (cloud_device));
66  pcl::oneapi::RandomSampleConsensus sac (sac_model);
67  sac.setMaxIterations (10000);
68  sac.setDistanceThreshold (0.03);
69  result = sac.computeModel ();
70
71  // Best model
72  pcl::oneapi::SampleConsensusModelPlane::Indices sample;
73  sac.getModel (sample);
74
75  // Coefficient
76  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeffs;
77  sac.getModelCoefficients (coeffs);
78
79  // Inliers
80  pcl::Indices pcl_inliers;
81  int inliers_size = sac.getInliersSize ();
82  pcl_inliers.resize(inliers_size);
83
84  pcl::oneapi::SampleConsensusModelPlane::IndicesPtr inliers = sac.getInliers ();
85  inliers->download(pcl_inliers.data(), 0, inliers_size);
86
87  // Refined coefficient
88  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeff_refined;
89  sac_model->optimizeModelCoefficients (*cloud_ptr, pcl_inliers, coeffs, coeff_refined);
90
91  // print log
92  std::cout << "input cloud size: " << cloud_ptr->points.size() << std::endl;
93  std::cout << "inliers size    : " << inliers_size << std::endl;
94  std::cout << "  plane model coefficient: " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;
95  std::cout << "  Optimized coefficient  : " << coeff_refined[0] << ", " << coeff_refined[1] << ", " << coeff_refined[2] << ", " << coeff_refined[3] << std::endl;
96}
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  3. Source the Intel® oneAPI Base Toolkit environment:

    source /opt/intel/oneapi/setvars.sh
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  4. (Optional) Setup proxy setting to download test data:

    export http_proxy="http://<http_proxy>:port"
export https_proxy="http://<https_proxy>:port"
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  5. Build the code:

    mkdir build && cd build
cmake ../
make -j
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  6. Run the binary:

    ./oneapi_passthrough
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  7. Expected results example:

    input cloud size: 307200
inliers size    : 77316
plane model coefficient: -0.0789502, -0.816661, -0.571692, 0.546386
Optimized coefficient  : -0.0722213, -0.818286, -0.570256, 0.547587
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Code Explanation

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

  // Read Point Cloud
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_ptr( new pcl::PointCloud<pcl::PointXYZ>() );
  pcl::PointCloud<pcl::PointXYZ>::Ptr convex_ptr;
  int result = pcl::io::loadPCDFile("test59.pcd", *cloud_ptr);
  if (result != 0)
  {
    pcl::console::print_info ("Load pcd file failed.\n");
    return result;
  }
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Create GPU input and output device arrays, and load point cloud data into the input device array.

  // Prepare Device Point Cloud Memory
  pcl::oneapi::SampleConsensusModel::PointCloud_xyz cloud_device_xyz;
  cloud_device_xyz.upload(cloud_ptr->points);
  pcl::oneapi::SampleConsensusModel::PointCloud & cloud_device = (pcl::oneapi::SampleConsensusModel::PointCloud &)cloud_device_xyz;
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Start computing the model.

  typename pcl::oneapi::SampleConsensusModelPlane::Ptr sac_model (new pcl::oneapi::SampleConsensusModelPlane (cloud_device));
  pcl::oneapi::RandomSampleConsensus sac (sac_model);
  sac.setMaxIterations (10000);
  sac.setDistanceThreshold (0.03);
  result = sac.computeModel ();
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Result (best model):

  pcl::oneapi::SampleConsensusModelPlane::Indices sample;
  sac.getModel (sample);
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Result (coefficient model):

  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeffs;
  sac.getModelCoefficients (coeffs);
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Result (inliers model):

  pcl::Indices pcl_inliers;
  int inliers_size = sac.getInliersSize ();
  pcl_inliers.resize(inliers_size);

  pcl::oneapi::SampleConsensusModelPlane::IndicesPtr inliers = sac.getInliers ();
  inliers->download(pcl_inliers.data(), 0, inliers_size);
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Result (refined coefficient model):

  pcl::oneapi::SampleConsensusModelPlane::Coefficients coeff_refined;
  sac_model->optimizeModelCoefficients (*cloud_ptr, pcl_inliers, coeffs, coeff_refined);
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Result (output log):

  std::cout << "input cloud size: " << cloud_ptr->points.size() << std::endl;
  std::cout << "inliers size    : " << inliers_size << std::endl;
  std::cout << "  plane model coefficient: " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;
  std::cout << "  Optimized coefficient  : " << coeff_refined[0] << ", " << coeff_refined[1] << ", " << coeff_refined[2] << ", " << coeff_refined[3] << std::endl;
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