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+---
+layout: page
+title: About
+permalink: /about/
+---
+
+What is PCL?
+============
+
+The Point Cloud Library (or PCL) is a large scale, open project [[1]](#open) for 2D/3D
+image and point cloud processing. The PCL framework contains numerous
+state-of-the art algorithms including filtering, feature estimation, surface
+reconstruction, registration, model fitting and segmentation. These algorithms
+can be used, for example, to filter outliers from noisy data, stitch 3D point
+clouds together, segment relevant parts of a scene, extract keypoints and
+compute descriptors to recognize objects in the world based on their geometric
+appearance, and create surfaces from point clouds and visualize them -- to name
+a few.
+
+PCL is released under the terms of the [3-clause BSD license](https://en.wikipedia.org/wiki/BSD_licenses#3-clause_license_.28.22Revised_BSD_License.22.2C_.22New_BSD_License.22.2C_or_.22Modified_BSD_License.22.29) and is open source software. **It is free for commercial and research use.** PCL is **cross-platform**,
+and has been successfully compiled and deployed on Linux, MacOS, Windows and
+Android. To simplify development, PCL is split into a series of smaller
+code libraries, that can be compiled separately. This modularity is important
+for distributing PCL on platforms with reduced computational or size
+constraints (for more information about each module see the [documentation]({{ site.api_base_url }})
+page). Another way to think about PCL is as a graph of code libraries,
+similar to the [Boost](http://www.boost.org/) set of C++ libraries. Here's an example:
+
+
+
+
+What is a Point Cloud?
+======================
+
+A point cloud is a data structure used to represent a collection of
+multi-dimensional points and is commonly used to represent three-dimensional
+data. In a 3D point cloud, the points usually represent the X, Y, and Z
+geometric coordinates of an underlying sampled surface. When color information
+is present (see the figures below), the point cloud becomes 4D.
+
+
+
+
+Point clouds can be acquired from hardware sensors such as stereo cameras, 3D scanners, or time-of-flight cameras, or generated from a computer program synthetically. PCL supports natively the OpenNI 3D interfaces, and can thus acquire and process data from devices such as the PrimeSensor 3D cameras, the Microsoft Kinect or the [Asus
+XTionPro](https://www.asus.com/3D-Sensor/Xtion_PRO/).
+
+For more information about point clouds and 3D processing please visit our [documentation]({{ site.api_base_url }}) page.
+
+References
+==========
+
+[1]
+For more information, including a scientific citation (more to be added soon), please see:
+
+{% raw %}
+ @InProceedings{Rusu_ICRA2011_PCL,
+ author = {Radu Bogdan Rusu and Steve Cousins},
+ title = {{3D is here: Point Cloud Library (PCL)}},
+ booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
+ month = {May 9-13},
+ year = {2011},
+ address = {Shanghai, China}
+ }
+{% endraw %}
+
+[Download PDF here](/assets/pdf/pcl_icra2011.pdf)
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+
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+---
+# Feel free to add content and custom Front Matter to this file.
+# To modify the layout, see https://jekyllrb.com/docs/themes/#overriding-theme-defaults
+
+layout: page
+---
+
+
+
+The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. PCL is released under the terms of the **BSD license**, and thus free for commercial and research use.
+
+Whether you've just discovered PCL or you're a long time veteran, this page contains links to a set of resources that will help consolidate your knowledge on PCL and 3D processing. An additional Wiki resource for developers is available at [https://github.com/PointCloudLibrary/pcl/wiki](https://github.com/PointCloudLibrary/pcl/wiki).
+
+Getting Started
+----------------
+
+To simplify both usage and development, we split PCL into a series of modular libraries. The most important set of released modules in PCL is shown below.
+
+
filters
features
keypoints
+
+
+
+
+
+
registration
kdtree
octree
+
+
+
+
+
+
segmentation
sample_consensus
surface
+
+
+
+
+
+
recognition
io
visualization
+
+
+
+
+
+
+
+
+[Tutorials][tutorials]
+----------------
+Our [comprehensive list of tutorials for PCL][tutorials], covers many topics, ranging from simple Point Cloud Input/Output operations to more complicated applications that include visualization, feature estimation, segmentation, etc.
+
+[][tutorials]
+
+
+[Advanced Topics][advanced]
+----------------
+
+If you're interested in how PCL works internally, or are looking at optimizing your workflow, we have assembled a [set of topics][tutorials] that cover interesting subjects from reducing your compile time to code profiling.
+
+[][advanced]
+
+
+[API Reference][api]
+----------------
+
+The [Application Programming Interface (API) documentation][api] for PCL includes explanations about how each public method and class works, shows simple code snippets that can be used quickly to prototype and test a particular algorithm, and links to scientific publications for those interested in finding out more about the theoretical aspects of a particular method.
+
+[api]: {{ site.api_base_url }}
+[advanced]: {{ site.advanced_base_url }}
+[tutorials]: {{ site.tutorials_base_url }}
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