PCL is a large cross platform open source c + + programming library based on the previous point cloud related research. It implements a large number of point cloud related general algorithms and efficient data structures, involving point cloud acquisition, filtering, segmentation, registration, retrieval, feature extraction, recognition, tracking, surface reconstruction, visualization, etc.
PCL（Point Cloud Library）是在吸收了前人点云相关研究基础上建立起来的大型跨平台开源 C++编程库，它实现了大量点云相关的通用算法和高效数据结构，涉及到点云获取、滤波、分割、配准、检索、特征提取、识别、追踪、曲面重建、可视化等。
PCL (point cloud Library) is a large cross platform open-source C + + programming library based on the previous point cloud related research. It implements a large number of point cloud related general algorithms and efficient data structures, involving point cloud acquisition, filtering, segmentation, registration, retrieval, feature extraction, recognition, tracking, surface reconstruction, visualization, etc.
支持多种操作系统平台，可在 Windows、Linux、Android、Mac OS X、部分嵌入式实时系统上运行。如果说 OpenCV 是 2D 信息获取与处理的结晶，那么 PCL 就在 3D 信息获取与处理上具有同等地位，PCL 是 BSD 授权方式，可以免费进行商业和学术应用。
It supports multiple operating system platforms and can run on windows, Linux, Android, Mac OS X and some embedded real-time systems. If opencv is the crystallization of 2D information acquisition and processing, PCL has the same status in 3D information acquisition and processing. PCL is a BSD authorization method, which can be used for free commercial and academic applications.
PCL 起初是 ROS（Robot Operating System）下由来自于慕尼黑工业大学（TUM – Technische Universität München）和斯坦福大学（Stanford University）Radu 博士等人维护和开发的开源项目，主要应用于机器人研究应用领域，随着各个算法模块的积累，于 2011 年独立出来，正式与全球 3D 信息获取、处理的同行一起，组建了强大的开发维护团队，以多所知名大学、研究所和相关硬件、软件公司为主。
PCL was originally an open-source project maintained and developed by Dr. Radu of tum – Technische University ä t m ü nchen and Stanford University under ROS (robot operating system), which was mainly applied to robot research and application fields. With the accumulation of various algorithm modules, PCL was independent in 2011, officially with global 3D Together with peers in information acquisition and processing, a strong development and maintenance team has been set up, focusing on a number of well-known universities, research institutes and related hardware and software companies.
发展非常迅速，不断有新的研究机构等加入，在 Willow Garage, NVidia, Google (GSOC 2011), Toyota, Trimble, Urban Robotics, Honda Research Institute 等多个全球知名公司的资金支持下，不断提出新的开发计划，代码更新非常活跃，至今在不到一年的时间内从 1.0 版本已经发布到 1.7.0 版本。
It has been developing very rapidly, and new research institutions are constantly joining in. In willow garage, NVIDIA, Google (gsoc 2011), Toyota, Trimble, urban robotics, With the financial support of many world-renowned companies such as Honda Research Institute, new development plans have been put forward constantly, and code updates are very active. So far, the version 1.0 has been released to version 1.7.0 in less than a year.
PCL 利用 OpenMP、GPU、CUDA 等先进高性能计算技术，通过并行化提高程序实时性。K 近邻搜索操作的构架是基于 FLANN (Fast Library for Approximate Nearest Neighbors)所实现的，速度也是技术中最快的。PCL 中的所有模块和算法都是通过 Boost 共享指针来传送数据的，因而避免了多次复制系统中已存在的数据的需要，从 0.6 版本开始，PCL 就已经被移入到 Windows，MacOS 和 Linux 系统，并且在 Android 系统也已经开始投入使用，这使得 PCL 的应用容易移植与多方发布。
PCL uses OpenMP, GPU, CUDA and other advanced high-performance computing technologies to improve the real-time performance of the program through parallelization. The architecture of k nearest neighbor search operation is based on FLANN (fast library for approximate nearest neighbors), and the speed is the fastest in the technology. All modules and algorithms in PCL transfer data through boost shared pointer, thus avoiding the need to duplicate existing data in the system for many times. Since version 0.6, PCL has been moved to windows, MacOS and Linux systems, and has been put into use in Android systems, which makes PCL applications easy to transplant and multi release.