PCL (Point Cloud Library)
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The document provides an overview of the Point Cloud Library (PCL), an open-source library for point cloud processing. PCL contains algorithms for filtering, segmentation, registration, surface reconstruction and more. It has a modular structure with libraries for filters, features, keypoints, visualization and I/O. PCL data can be read from and written to various file formats like PCD and PLY. It also integrates with MeshLab for point cloud visualization. Examples are given to demonstrate converting a PCD file to PLY format using PCL.
![PCL Structure:
9/37
Point Types in PCL:
• PointXYZ - Members: float x, y, z; most used.
• PointXYZI - Members: float x, y, z, intensity; Simple XYZ + intensity.
• PointXYZRGBA - Members: float x, y, z; uint32_t rgba; RGBA information packed into a
single integer.
• PointXYZRGB - float x, y, z, rgb; RGBA information packed into a float.
• PointNormal - float x, y, z; float normal[3], curvature; surface normals and curvatures.
• ……..
• Not enough! Defining your own custom PointT type.](https://image.slidesharecdn.com/pcl-180329173749/85/PCL-Point-Cloud-Library-11-320.jpg)
PCL (Point Cloud Library)
- 1. Point Cloud Library Soubhi Hadri Department of Electrical and Computer Engineering University of Oklahoma March, 2018 Computer Vision
- 2. Outline: • Introduction • Point Cloud Overview • PCL (Point Cloud Library) Structure • PCL Walkthrough • MeshLab • PCL Installation • Example
- 3. Introduction : 1/37 What is a point cloud? • A point cloud is a data structure used to represent a collection of multi-dimensional points (usually in 3D X,Y,Z). • Used to represent 3D information about the world • Each 3D point can contain more information like: RGB color, distance, intensity …
- 4. Introduction : 2/37 What is a point cloud?
- 5. Introduction : 3/37 How is point cloud generated? Point clouds can be acquired from hardware sensors: • Stereo Cameras • 3D Scanners • Time-of-flight cameras • Generated from a computer program • …..
- 6. PCL Overview: 4/37 Point Cloud Library (or PCL): • Large scale, open project for 2D/3D image and point cloud processing. • Cross-platform • Contains numerous state-of-the art algorithms : • Filtering • Feature Estimation • Surface Reconstruction • Registration • segmentation • … • Under BSD license and is open source software. It is free for commercial and research use.
- 7. PCL Overview: 5/37 Who is developing PCL: • Many organizations all around the world including :
- 8. PCL Overview: 6/37 Who is developing PCL: • Many organizations all around the world including :
- 9. PCL Overview: 7/37 Who is financially supporting PCL: • Toyota , Google, Nvidia, Intel, ….
- 10. PCL Structure : 8/37 Point Cloud Library (or PCL): • PCL is split into a series of smaller code libraries: • It can be compiled separately. • Think about PCL is as a graph of code libraries.
- 11. PCL Structure: 9/37 Point Types in PCL: • PointXYZ - Members: float x, y, z; most used. • PointXYZI - Members: float x, y, z, intensity; Simple XYZ + intensity. • PointXYZRGBA - Members: float x, y, z; uint32_t rgba; RGBA information packed into a single integer. • PointXYZRGB - float x, y, z, rgb; RGBA information packed into a float. • PointNormal - float x, y, z; float normal[3], curvature; surface normals and curvatures. • …….. • Not enough! Defining your own custom PointT type.
- 12. PCL Walkthrough: 10/37 PCL is split in a number of modular libraries. The most important : • Filters • Features • Keypoints • Registration • Kdtree • Octree • Visualization • I/O • Common • …
- 13. PCL Walkthrough: Filters 11/37 Example : noise removal • Some of these outliers can be filtered by performing a statistical analysis on each point’s neighborhood. • Trimming those that do not meet a certain criteria.
- 14. PCL Walkthrough: Filters 12/37 Downsampling a PointCloud using a VoxelGrid filter Reduce the number of points – a point cloud dataset, using a voxelized grid approach.
- 15. PCL Walkthrough: Filters 13/37 Removing outliers using a StatisticalOutlierRemoval filter Remove noisy measurements, e.g. outliers, from a point cloud dataset using statistical analysis techniques.
- 16. PCL Walkthrough: Segmentation 14/37 • Algorithms for segmenting a point cloud into distinct clusters. • These algorithms are best suited for processing a point cloud that is composed of a number of spatially isolated regions. • Clustering is often used to break the cloud down into its constituent parts, which can then be processed independently. plane model segmentation cylinder model segmentation
- 17. PCL Walkthrough: Surface • Deals with reconstructing the original surfaces from 3D scans. Surface Triangulation obtain a triangle mesh based on projections of the local neighborhoods. 15/37
- 18. PCL Walkthrough: Surface • Calculate a simple 2D hull polygon (concave or convex) for a set of points supported by a plane. • Creating a convex or concave hull is helpful: • Simplifying the surface representation. • When boundaries need to be extracted. 16/37
- 19. PCL Walkthrough: Surface • B-spline fitting algorithm to obtain a smooth, parametric surface representation. 17/37
- 20. PCL Walkthrough: Visualization • Quickly prototype and visualize the results of algorithms operating on 3D point cloud data. • Similar to OpenCV’s highgui routines for displaying 2D images and for drawing basic 2D shapes on screen. Rendering and setting visual properties (colors, point sizes, opacity, etc) for any n-D point cloud datasets 18/37
- 21. PCL Walkthrough: Visualization Drawing basic 3D shapes on screen (e.g., cylinders, spheres,lines, polygons, etc) • Quickly prototype and visualize the results of algorithms operating on 3D point cloud data. • Similar to OpenCV’s highgui routines for displaying 2D images and for drawing basic 2D shapes on screen. 19/37
- 22. PCL Walkthrough: Visualization Histogram visualization module (PCLHistogramVisualizer) for 2D plots • Quickly prototype and visualize the results of algorithms operating on 3D point cloud data. • Similar to OpenCV’s highgui routines for displaying 2D images and for drawing basic 2D shapes on screen. 20/37
- 23. PCL Walkthrough: Visualization RangeImage visualization module • Quickly prototype and visualize the results of algorithms operating on 3D point cloud data. • Similar to OpenCV’s highgui routines for displaying 2D images and for drawing basic 2D shapes on screen. 21/37
- 24. PCL Walkthrough: I/O • Contains classes and functions for reading and writing point cloud data (PCD/PLY/…) files. • Capturing point clouds from a variety of sensing devices. PCD (Point Cloud Data) point clouds can be stored to disk as files, into the PCD format. PCD file consists of: • Header: • VERSION - specifies the PCD file version • FIELDS - specifies the name of each dimension/field that a point can have. • SIZE - specifies the size of each dimension in bytes • TYPE - specifies the type of each dimension as a char. • COUNT - specifies how many elements does each dimension have • WIDTH - specifies the width of the point cloud dataset in the number of points • HEIGHT - specifies the height of the point cloud dataset in the number of points. 22/37
- 25. MeshLab: • Open source system for processing and editing 3D triangular meshes. • Available : Mac, Windows, Linux and Source. • You can use it to show point clouds. 23/37
- 26. MeshLab: Drag & Drop PCD file 24/37
- 27. PCL Installation: • Depends on 3rd party libraries: 1. Boost 2. Eigen 3. FLANN 4. VTK • Prebuilt binaries are available for Linux, Windows, and Mac OS X. • To build PCL out of sources: 1. Clone code from github. 2. PCL relies on CMake as a build tool. 25/37
- 28. PCL Installation: 26/37 • Set the configurations 3 5 1 2 4
- 29. PCL Installation: • Configure. • Generate. • Go to _build and execute make 27/37
- 30. PCL Installation: • Execute make install in _build 28/37
- 31. PCL Example: To convert PCD to PLY: 1. C++ main code. 29/37
- 32. PCL Example: To convert PCD to PLY: 1. C++ main code. 2. Cmakefile. 30/37
- 33. PCL Example: To convert PCD to PLY: 1. C++ main code. 2. Cmakefile. 3. Cmake-GUI configure + generate. 31/37
- 34. PCL Example: To convert PCD to PLY: 1. C++ main code. 2. Cmakefile. 3. Cmake-GUI configure + generate. 4. In _build folder execute make . 32/37
- 35. PCL Example: To convert PCD to PLY: 1. C++ main code. 2. Cmakefile. 3. Cmake-GUI configure + generate. 4. In _build folder execute make . 5. Run pcl_example and check for ply output file 33/37
- 37. Resources: PCL official website : pointclouds.org. MeshLab : meshlab.net. 35/37
- 38. Script on GitHub: PCD-2-PLY 36/37
- 39. Thank you 37/37