Plane Model Segmentation#

In this tutorial, we will explore the process of simple plane segmentation, extracting points within a point cloud that contribute to a plane model.

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>/segmentation

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

     1// SPDX-License-Identifier: Apache-2.0
 2// Copyright (C) 2025 Intel Corporation
 3#include <pcl/oneapi/segmentation/segmentation.h>
 4#include <pcl/io/pcd_io.h>
 5#include <pcl/point_types.h>
 6
 7int main (int argc, char **argv)
 8{
 9    std::cout << "Running on device: " << dpct::get_default_queue().get_device().get_info<sycl::info::device::name>() << "\n";
10
11    //Read Point Cloud
12    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_input (new pcl::PointCloud<pcl::PointXYZ> ());
13
14    //Load a standard PCD file from disk
15    int result = pcl::io::loadPCDFile("test59.pcd", *cloud_input);
16    if (result != 0)
17    {
18        pcl::console::print_info ("Load pcd file failed.\n");
19        return result;
20    }
21
22    //Create the oneapi_segmentation object
23    pcl::oneapi::SACSegmentation seg;
24
25    //Configure oneapi_segmentation class 
26    seg.setInputCloud(cloud_input);
27    seg.setProbability(0.99);
28    seg.setMaxIterations(50);
29    seg.setDistanceThreshold(0.01);
30    //Optional  
31    seg.setOptimizeCoefficients(true);
32    //Set algorithm method and model type 
33    seg.setMethodType(pcl::oneapi::SAC_RANSAC);
34    seg.setModelType (pcl::oneapi::SACMODEL_PLANE);
35
36    //Out parameter declaration for getting inliers and model coefficients  
37    pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
38    double coeffs[4]={0,0,0,0};
39
40    //Getting inliers and model coefficients
41    seg.segment(*inliers, coeffs);
42
43    std::cout << "input cloud size   : " << seg.getCloudSize() << std::endl;
44    std::cout << "inliers size       : " << seg.getInliersSize() << std::endl;
45    std::cout << "model coefficients : " << coeffs[0] << ", " << coeffs[1] << ", " << coeffs[2] << ", " << coeffs[3] << std::endl;
46
47    return 0;
48}

  3. Source the Intel® oneAPI Base Toolkit environment:

    source /opt/intel/oneapi/setvars.sh

  4. (Optional) Set up 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;