The following links describe a set of basic PCL tutorials. Please note that their source codes may already be provided as part of the PCL regular releases, so check there before you start copy & pasting the code. The list of tutorials below is automatically generated from reST files located in our git repository.

Note

Before you start reading, please make sure that you go through the higher-level overview documentation at http://www.pointclouds.org/documentation/, under Getting Started. Thank you.

Basic Usage

• PCL Walkthrough

 Title: PCL Functionality Walkthrough Author: Razvan G. Mihalyi Compatibility: > PCL 1.6 Takes the reader through all of the PCL modules and offers basic explanations on their functionalities.
• Getting Started / Basic Structures

 Title: Getting Started / Basic Structures Author: Radu B. Rusu Compatibility: > PCL 1.0 Presents the basic data structures in PCL and discusses their usage with a simple code example.
• Using PCL in your own project

 Title: Using PCL in your own project Author: Nizar Sallem Compatibility: > PCL 1.0 In this tutorial, we will learn how to link your own project to PCL using cmake.
• Compiling PCL from source on POSIX compliant systems

 Title: Compiling PCL from source on POSIX compliant systems Author: Victor Lamoine Compatibility: > PCL 1.0 In this tutorial, we will explain how to compile PCL from sources on POSIX/Unix systems.
• Customizing the PCL build process

 Title: Explaining PCL’s cmake options Author: Nizar Sallem Compatibility: > PCL 1.0 In this tutorial, we will explain the basic PCL cmake options, and ways to tweak them to fit your project.
• Building PCL’s dependencies from source on Windows

 Title: Compiling PCL’s dependencies from source on Windows Authors: Alessio Placitelli and Mourad Boufarguine Compatibility: > PCL 1.0 In this tutorial, we will explain how to compile PCL’s 3rd party dependencies from source on Microsoft Windows.
• Compiling PCL from source on Windows

 Title: Compiling PCL on Windows Author: Mourad Boufarguine Compatibility: > PCL 1.0 In this tutorial, we will explain how to compile PCL on Microsoft Windows.
• Compiling PCL and its dependencies from MacPorts and source on Mac OS X

 Title: Compiling PCL and its dependencies from MacPorts and source on Mac OS X Author: Justin Rosen Compatibility: > PCL 1.0 This tutorial explains how to build the Point Cloud Library from MacPorts and source on Mac OS X platforms.
• Installing on Mac OS X using Homebrew

 Title: Installing on Mac OS X using Homebrew Author: Geoffrey Biggs Compatibility: > PCL 1.2 This tutorial explains how to install the Point Cloud Library on Mac OS X using Homebrew. Both direct installation and compiling PCL from source are explained.
• Using PCL with Eclipse

 Title: Using Eclipse as your PCL editor Author: Koen Buys Compatibility: PCL git master This tutorial shows you how to get your PCL as a project in Eclipse.
• Generate a local documentation for PCL

 Title: Generate a local documentation for PCL Author: Victor Lamoine Compatibility: PCL > 1.0 This tutorial shows you how to generate and use a local documentation for PCL.
• Using a matrix to transform a point cloud

 Title: Using matrixes to transform a point cloud Author: Victor Lamoine Compatibility: > PCL 1.5 This tutorial shows you how to transform a point cloud using a matrix.

 Title: Adding your own custom PointT point type Author: Radu B. Rusu Compatibility: > PCL 0.9, < PCL 2.0 This document explains what templated point types are in PCL, why do they exist, and how to create and use your own PointT point type.
• Writing a new PCL class

 Title: Writing a new PCL class Author: Radu B. Rusu, Luca Penasa Compatibility: > PCL 0.9, < PCL 2.0 This short guide is to serve as both a HowTo and a FAQ for writing new PCL classes, either from scratch, or by adapting old code.

Features

• How 3D Features work in PCL

 Title: How 3D features work Author: Radu B. Rusu Compatibility: > PCL 1.0 This document presents a basic introduction to the 3D feature estimation methodologies in PCL.
• Estimating Surface Normals in a PointCloud

 Title: Estimating Surface Normals in a PointCloud Author: Radu B. Rusu Compatibility: > PCL 1.0 This tutorial discusses the theoretical and implementation details of the surface normal estimation module in PCL.
• Normal Estimation Using Integral Images

 Title: Normal Estimation Using Integral Images Author: Stefan Holzer Compatibility: > PCL 1.0 In this tutorial we will learn how to compute normals for an organized point cloud using integral images.
• Point Feature Histograms (PFH) descriptors

 Title: Point Feature Histograms (PFH) descriptors Author: Radu B. Rusu Compatibility: > PCL 1.0 This tutorial introduces a family of 3D feature descriptors called PFH (Point Feature Histograms) and discusses their implementation details from PCL’s perspective.
• Fast Point Feature Histograms (FPFH) descriptors

 Title: Fast Point Feature Histograms (FPFH) descriptors Author: Radu B. Rusu Compatibility: > PCL 1.3 This tutorial introduces the FPFH (Fast Point Feature Histograms) 3D descriptor and discusses their implementation details from PCL’s perspective.
• Estimating VFH signatures for a set of points

 Title: Estimating VFH signatures for a set of points Author: Radu B. Rusu Compatibility: > PCL 0.8 This document describes the Viewpoint Feature Histogram (VFH) descriptor, a novel representation for point clusters for the problem of Cluster (e.g., Object) Recognition and 6DOF Pose Estimation.
• How to extract NARF Features from a range image

 Title: How to extract NARF features from a range image Author: Bastian Steder Compatibility: > 1.3 In this tutorial, we will learn how to extract NARF features from a range image.
• Moment of inertia and eccentricity based descriptors

 Title: Moment of inertia and eccentricity based descriptors Author: Sergey Ushakov Compatibility: > PCL 1.7 In this tutorial we will learn how to compute moment of inertia and eccentricity of the cloud. In addition to this we will learn how to extract AABB and OBB.
• RoPs (Rotational Projection Statistics) feature

 Title: RoPs (Rotational Projection Statistics) feature Author: Sergey Ushakov Compatibility: > PCL 1.7 In this tutorial we will learn how to compute RoPS feature.
• Globally Aligned Spatial Distribution (GASD) descriptors

 Title: Globally Aligned Spatial Distribution (GASD) descriptors Author: Joao Paulo Lima Compatibility: >= PCL 1.9 This document describes the Globally Aligned Spatial Distribution (GASD) global descriptor to be used for efficient object recognition and pose estimation.

Filtering

• Filtering a PointCloud using a PassThrough filter

 Title: Filtering a PointCloud using a PassThrough filter Author: Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to remove points whose values fall inside/outside a user given interval along a specified dimension.
• Downsampling a PointCloud using a VoxelGrid filter

 Title: Downsampling a PointCloud using a VoxelGrid filter Author: Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to downsample (i.e., reduce the number of points) a Point Cloud.
• Removing outliers using a StatisticalOutlierRemoval filter

 Title: Removing sparse outliers using StatisticalOutlierRemoval Author: Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to remove sparse outliers from noisy data, using StatisticalRemoval.
• Projecting points using a parametric model

 Title: Projecting points using a parametric model Author: Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to project points to a parametric model (i.e., plane).
• Extracting indices from a PointCloud

 Title: Extracting indices from a PointCloud Author: Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to extract a set of indices given by a segmentation algorithm.
• Removing outliers using a Conditional or RadiusOutlier removal

 Title: Removing outliers using a Conditional or RadiusOutlier removal Author: Gabe O’Leary Compatibility: > PCL 1.0 In this tutorial, we will learn how to remove outliers from noisy data, using ConditionalRemoval, RadiusOutlierRemoval.

I/O

• The PCD (Point Cloud Data) file format

 Title: The PCD (Point Cloud Data) file format Author: Radu B. Rusu Compatibility: > PCL 0.9 This document describes the PCD file format, and the way it is used inside PCL.
• Reading Point Cloud data from PCD files

 Title: Reading Point Cloud data from PCD files Author: Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to read a Point Cloud from a PCD file.
• Writing Point Cloud data to PCD files

 Title: Writing Point Cloud data to PCD files Author: Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to write a Point Cloud to a PCD file.
• Concatenate the points of two Point Clouds

 Title: Concatenate the fields or points of two Point Clouds Author: Gabe O’Leary / Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial, we will learn how to concatenate both the fields and the point data of two Point Clouds. When concatenating fields, one PointClouds contains only XYZ data, and the other contains Surface Normal information.
• The OpenNI Grabber Framework in PCL

 Title: Grabbing Point Clouds from an OpenNI camera Author: Nico Blodow Compatibility: > PCL 1.0 In this tutorial, we will learn how to acquire point cloud data from an OpenNI camera.
• The Velodyne High Definition LiDAR (HDL) Grabber

 Title: Grabbing Point Clouds from a Velodyne High Definition LiDAR (HDL) Author: Keven Ring Compatibility: >= PCL 1.7 In this tutorial, we will learn how to acquire point cloud data from a Velodyne HDL.
• The PCL Dinast Grabber Framework

 Title: Grabbing Point Clouds from Dinast Cameras Author: Marco A. Gutierrez Compatibility: >= PCL 1.7 In this tutorial, we will learn how to acquire point cloud data from a Dinast camera.
• Grabbing point clouds from Ensenso cameras

 Title: Grabbing point clouds from Ensenso cameras Author: Victor Lamoine Compatibility: >= PCL 1.8.0 In this tutorial, we will learn how to acquire point cloud data from an IDS-Imaging Ensenso camera.
• Grabbing point clouds / meshes from davidSDK scanners

 Title: Grabbing point clouds / meshes from davidSDK scanners Author: Victor Lamoine Compatibility: >= PCL 1.8.0 In this tutorial, we will learn how to acquire point cloud or mesh data from a davidSDK scanner.
• Grabbing point clouds from DepthSense cameras

 Title: Grabbing point clouds from DepthSense cameras Author: Sergey Alexandrov Compatibility: >= PCL 1.8.0 In this tutorial we will learn how to setup and use DepthSense cameras within PCL on both Linux and Windows platforms.

Keypoints

• How to extract NARF keypoint from a range image

 Title: How to extract NARF keypoints from a range image Author: Bastian Steder Compatibility: > 1.3 In this tutorial, we will learn how to extract NARF keypoints from a range image.

KdTree

• How to use a KdTree to search

 Title: KdTree Search Author: Gabe O’Leary Compatibility: > PCL 1.0 In this tutorial, we will learn how to search using the nearest neighbor method for k-d trees

Octree

• Point Cloud Compression

 Title: Point cloud compression Author: Julius Kammerl Compatibility: > PCL 1.0 In this tutorial, we will learn how to compress a single point cloud and streams of point clouds.
• Spatial Partitioning and Search Operations with Octrees

 Title: Octrees for spatial partitioning and neighbor search Author: Julius Kammerl Compatibility: > PCL 1.0 In this tutorial, we will learn how to use octrees for spatial partitioning and nearest neighbor search.
• Spatial change detection on unorganized point cloud data

 Title: Spatial change detection on unorganized point cloud data Author: Julius Kammerl Compatibility: > PCL 1.0 In this tutorial, we will learn how to use octrees for detecting spatial changes within point clouds.

Range Images

• How to create a range image from a point cloud

 Title: Creating Range Images from Point Clouds Author: Bastian Steder Compatibility: > PCL 1.0 This tutorial demonstrates how to create a range image from a point cloud and a given sensor position.
• How to extract borders from range images

 Title: Extracting borders from Range Images Author: Bastian Steder Compatibility: > PCL 1.3 This tutorial demonstrates how to extract borders (traversals from foreground to background) from a range image.

Recognition

• 3D Object Recognition based on Correspondence Grouping

 Title: The PCL Recognition API Author: Tommaso Cavallari, Federico Tombari Compatibility: > PCL 1.6 This tutorial aims at explaining how to perform 3D Object Recognition based on the pcl_recognition module.
• Implicit Shape Model

 Title: Implicit Shape Model Author: Sergey Ushakov Compatibility: > PCL 1.7 In this tutorial we will learn how the Implicit Shape Model algorithm works and how to use it for finding objects centers.
• Tutorial: Hypothesis Verification for 3D Object Recognition

 Title: Hypothesis Verification for 3D Object Recognition Author: Daniele De Gregorio, Federico Tombari Compatibility: > PCL 1.7 This tutorial aims at explaining how to do 3D object recognition in clutter by verifying model hypotheses in cluttered and heavily occluded 3D scenes.

Registration

• The PCL Registration API

 Title: The PCL Registration API Author: Dirk Holz, Radu B. Rusu, Jochen Sprickerhof Compatibility: > PCL 1.5 In this document, we describe the point cloud registration API and its modules: the estimation and rejection of point correspondences, and the estimation of rigid transformations.
• How to use iterative closest point

 Title: How to use iterative closest point algorithm Author: Gabe O’Leary Compatibility: > PCL 1.0 This tutorial gives an example of how to use the iterative closest point algorithm to see if one PointCloud is just a rigid transformation of another PointCloud.
• How to incrementally register pairs of clouds

 Title: How to incrementally register pairs of clouds Author: Raphael Favier Compatibility: > PCL 1.4 This document demonstrates using the Iterative Closest Point algorithm in order to incrementally register a series of point clouds two by two.
• Interactive Iterative Closest Point

 Title: Interactive ICP Author: Victor Lamoine Compatibility: > PCL 1.5 This tutorial will teach you how to build an interactive ICP program
• How to use Normal Distributions Transform

 Title: How to use the Normal Distributions Transform algorithm Author: Brian Okorn Compatibility: > PCL 1.6 This document demonstrates using the Normal Distributions Transform algorithm to register two large point clouds.
• In-hand scanner for small objects

 Title: How to use the In-hand scanner for small objects Author: Martin Saelzle Compatibility: >= PCL 1.7 This document shows how to use the In-hand scanner applications to obtain colored models of small objects with RGB-D cameras.
• Robust pose estimation of rigid objects

 Title: Robust pose estimation of rigid objects Author: Anders Glent Buch Compatibility: >= PCL 1.7 In this tutorial, we show how to find the alignment pose of a rigid object in a scene with clutter and occlusions.

Sample Consensus

• How to use Random Sample Consensus model

 Title: How to use Random Sample Consensus model Author: Gabe O’Leary Compatibility: > PCL 1.0 In this tutorial we learn how to use a RandomSampleConsensus with a plane model to obtain the cloud fitting to this model.

Segmentation

• Plane model segmentation

 Title: Plane model segmentation Author: Radu B. Rusu Compatibility: > PCL 1.3 In this tutorial, we will learn how to segment arbitrary plane models from a given point cloud dataset.
• Cylinder model segmentation

 Title: Cylinder model segmentation Author: Radu B. Rusu Compatibility: > PCL 1.3 In this tutorial, we will learn how to segment arbitrary cylindrical models from a given point cloud dataset.
• Euclidean Cluster Extraction

 Title: Euclidean Cluster Extraction Author: Serkan Tuerker Compatibility: > PCL 1.3 In this tutorial we will learn how to extract Euclidean clusters with the pcl::EuclideanClusterExtraction class.
• Region growing segmentation

 Title: Region Growing Segmentation Author: Sergey Ushakov Compatibility: >= PCL 1.7 In this tutorial we will learn how to use region growing segmentation algorithm.
• Color-based region growing segmentation

 Title: Color-based Region Growing Segmentation Author: Sergey Ushakov Compatibility: >= PCL 1.7 In this tutorial we will learn how to use color-based region growing segmentation algorithm.
• Min-Cut Based Segmentation

 Title: Min-Cut Based Segmentation Author: Sergey Ushakov Compatibility: >= PCL 1.7 In this tutorial we will learn how to use min-cut based segmentation algorithm.
• Conditional Euclidean Clustering

 Title: Conditional Euclidean Clustering Author: Frits Florentinus Compatibility: >= PCL 1.7 This tutorial describes how to use the Conditional Euclidean Clustering class in PCL: A segmentation algorithm that clusters points based on Euclidean distance and a user-customizable condition that needs to hold.
• Difference of Normals Based Segmentation

 Title: Difference of Normals Based Segmentation Author: Yani Ioannou Compatibility: >= PCL 1.7 In this tutorial we will learn how to use the difference of normals feature for segmentation.
• Clustering of Pointclouds into Supervoxels - Theoretical primer

 Title: Supervoxel Clustering Author: Jeremie Papon Compatibility: >= PCL 1.8 In this tutorial, we show to break a pointcloud into the mid-level supervoxel representation.
• Identifying ground returns using ProgressiveMorphologicalFilter segmentation

 Title: Progressive Morphological Filtering Author: Brad Chambers Compatibility: >= PCL 1.8 In this tutorial, we show how to segment a point cloud into ground and non-ground returns.
• Filtering a PointCloud using ModelOutlierRemoval

 Title: Model outlier removal Author: Timo Häckel Compatibility: >= PCL 1.7.2 This tutorial describes how to extract points from a point cloud using SAC models

Surface

• Smoothing and normal estimation based on polynomial reconstruction

 Title: Smoothing and normal estimation based on polynomial reconstruction Author: Zoltan-Csaba Marton, Alexandru E. Ichim Compatibility: > PCL 1.6 In this tutorial, we will learn how to construct and run a Moving Least Squares (MLS) algorithm to obtain smoothed XYZ coordinates and normals.
• Construct a concave or convex hull polygon for a plane model

 Title: Construct a concave or convex hull polygon for a plane model Author: Gabe O’Leary, Radu B. Rusu Compatibility: > PCL 1.0 In this tutorial we will learn how to calculate a simple 2D concave or convex hull polygon for a set of points supported by a plane.
• Fast triangulation of unordered point clouds

 Title: Fast triangulation of unordered point clouds Author: Zoltan-Csaba Marton Compatibility: > PCL 1.0 In this tutorial we will learn how to run a greedy triangulation algorithm on a PointCloud with normals to obtain a triangle mesh based on projections of the local neighborhood.
• Fitting trimmed B-splines to unordered point clouds

 Title: Fitting trimmed B-splines to unordered point clouds Author: Thomas Mörwald Compatibility: > PCL 1.7 In this tutorial we will learn how to reconstruct a smooth surface from an unordered point-cloud by fitting trimmed B-splines.

Visualization

• The CloudViewer

 Title: Visualizing Point Clouds Author: Ethan Rublee Compatibility: > PCL 1.0 This tutorial demonstrates how to use the pcl visualization tools.
• How to visualize a range image

 Title: Visualizing Range Images Author: Bastian Steder Compatibility: > PCL 1.3 This tutorial demonstrates how to use the pcl visualization tools for range images.
• PCLVisualizer

 Title: PCLVisualizer Author: Geoffrey Biggs Compatibility: > PCL 1.3 This tutorial demonstrates how to use the PCLVisualizer class for powerful visualisation of point clouds and related data.
• PCLPlotter

 Title: PCLPlotter Author: Kripasindhu Sarkar Compatibility: > PCL 1.7 This tutorial demonstrates how to use the PCLPlotter class for powerful visualisation of plots, charts and histograms of raw data and explicit functions.
• Visualization

 Title: PCL Visualization overview Author: Radu B. Rusu Compatibility: >= PCL 1.0 This tutorial will give an overview on the usage of the PCL visualization tools.
• Create a PCL visualizer in Qt with cmake

 Title: Create a PCL visualizer in Qt with cmake Author: Victor Lamoine Compatibility: > PCL 1.5 This tutorial shows you how to create a PCL visualizer within a Qt application.
• Create a PCL visualizer in Qt to colorize clouds

 Title: Create a PCL visualizer in Qt to colorize clouds Author: Victor Lamoine Compatibility: > PCL 1.5 This tutorial shows you how to color point clouds within a Qt application.

Applications

• Aligning object templates to a point cloud

 Title: Aligning object templates to a point cloud Author: Michael Dixon Compatibility: > PCL 1.3 This tutorial gives an example of how some of the tools covered in the previous tutorials can be combined to solve a higher level problem — aligning a previously captured model of an object to some newly captured data.
• Cluster Recognition and 6DOF Pose Estimation using VFH descriptors

 Title: Cluster Recognition and 6DOF Pose Estimation using VFH descriptors Author: Radu B. Rusu Compatibility: > PCL 0.8 In this tutorial we show how the Viewpoint Feature Histogram (VFH) descriptor can be used to recognize similar clusters in terms of their geometry.
• Point Cloud Streaming to Mobile Devices with Real-time Visualization

 Title: Point Cloud Streaming to Mobile Devices with Real-time Visualization Author: Pat Marion Compatibility: > PCL 1.3 This tutorial describes how to send point cloud data over the network from a desktop server to a client running on a mobile device.
• Detecting people on a ground plane with RGB-D data

 Title: Detecting people on a ground plane with RGB-D data Author: Matteo Munaro Compatibility: >= PCL 1.7 This tutorial presents a method for detecting people on a ground plane with RGB-D data.