PCL Developers blog

Construct a concave hull polygon

In this tutorial we will learn how to calculate a simple 2D concave hull polygon for a set of points supported by a plane.

The code

First download the dataset and save it somewher to disk. Then, create a file, let’s say, concave_hull_2d.cpp in your favorite editor and place the following inside:

#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/filters/passthrough.h>
#include <pcl/filters/project_inliers.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/surface/concave_hull.h>

 main (int argc, char** argv)
    cloud (new pcl::PointCloud<pcl::PointXYZ>),
    cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>),
    cloud_projected (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PCDReader reader;
  reader.read ("table_scene_mug_stereo_textured.pcd", *cloud);

  // Build a filter to remove spurious NaNs
  pcl::PassThrough<pcl::PointXYZ> pass;
  pass.setInputCloud (cloud);
  pass.setFilterFieldName ("z");
  pass.setFilterLimits (0, 1.1);
  pass.filter (*cloud_filtered);
  std::cerr << "PointCloud after filtering has: " <<
    cloud_filtered->points.size () << " data points." << std::endl;

  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
  pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
  // Create the segmentation object
  pcl::SACSegmentation<pcl::PointXYZ> seg;
  // Optional
  seg.setOptimizeCoefficients (true);
  // Mandatory
  seg.setModelType (pcl::SACMODEL_PLANE);
  seg.setMethodType (pcl::SAC_RANSAC);
  seg.setDistanceThreshold (0.01);

  seg.setInputCloud (cloud_filtered);
  seg.segment (*inliers, *coefficients);

  // Project the model inliers
  pcl::ProjectInliers<pcl::PointXYZ> proj;
  proj.setModelType (pcl::SACMODEL_PLANE);
  proj.setInputCloud (cloud_filtered);
  proj.setModelCoefficients (coefficients);
  proj.filter (*cloud_projected);

  // Create a concave Hull representation of the projected inliers
    cloud_hull (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::concaveHull<pcl::PointXYZ> chull;
  chull.setInputCloud (cloud_projected);
  chull.reconstruct (*cloud_hull);

  std::cerr << "Concave hull has: " << cloud_hull->points.size () <<
     " data points." << std::endl;

  pcl::PCDWriter writer;
  writer.write ("table_scene_mug_stereo_textured_hull.pcd", *cloud_hull,

  return (0);

The explanation

The only interesting part is in the lines below, where the ConcaveHull object gets created and the reconstruction is performed:

  cloud_hull (new pcl::PointCloud<pcl::PointXYZ>);
pcl::ConcaveHull<pcl::PointXYZ> chull;
chull.setInputCloud (cloud_projected);
chull.reconstruct (*cloud_hull);

Compiling and running the program

Add the following lines to your CMakeLists.txt file:

cmake_minimum_required(VERSION 2.8 FATAL_ERROR)


find_package(PCL 1.0 REQUIRED)


add_executable (concave_hull_2d concave_hull_2d.cpp)
target_link_libraries (concave_hull_2d ${PCL_LIBRARIES})

After you have made the executable, you can run it. Simply do:

$ ./concave_hull_2d

You will see something similar to:

PointCloud after filtering has: 139656 data points.
Concave hull has: 30 data points.