使用 Homebrew, apk, dnf, Nix, apt 安装 pcl

项目链接

• 主页
• 仓库
• 软件包管理器页面

命令和别名

• pcl_add_gaussian_noise
• pcl_bilateral_upsampling
• pcl_boundary_estimation
• pcl_cloud_composer
• pcl_cluster_extraction
• pcl_compute_cloud_error
• pcl_compute_hausdorff
• pcl_compute_hull
• pcl_concatenate_points_pcd
• pcl_convert_pcd_ascii_binary
• pcl_converter
• pcl_convolve
• pcl_crf_segmentation
• pcl_crop_to_hull
• pcl_demean_cloud
• pcl_dinast_grabber
• pcl_elch
• pcl_extract_feature
• pcl_face_trainer
• pcl_fast_bilateral_filter
• pcl_feature_matching
• pcl_fpfh_estimation
• pcl_fs_face_detector
• pcl_generate
• pcl_gp3_surface
• pcl_grabcut_2d
• pcl_grid_min
• pcl_hdl_grabber
• pcl_hdl_viewer_simple
• pcl_icp
• pcl_icp2d
• pcl_linemod_detection

项目历史

PCL began at Willow Garage around 2010 as a large-scale open library for 2D/3D image and point-cloud processing. Radu Bogdan Rusu and Steve Cousins presented the project at the 2011 IEEE International Conference on Robotics and Automation.

Copyright notices in the official source record the transition from Willow Garage stewardship to Open Perception from 2012 onward. The project remains BSD-licensed and supported by commercial organizations, researchers, donors, and contributors.

采用历史

PCL emerged alongside inexpensive 3D sensors and growing robotics demand for reusable perception algorithms. Its project paper describes an international community of robotics and perception researchers, and official project material identifies both commercial and research use.

The library became particularly visible in robotics workflows, while its cross-platform, modular design enabled deployment on Linux, macOS, Windows, Android, and size-constrained systems. Official tutorials also document package-manager installation through Homebrew.

使用方式

Applications link the required PCL modules rather than the entire framework, while bundled commands handle common conversion, filtering, registration, reconstruction, feature-extraction, and visualization tasks.

The official tutorials cover CMake integration, PCD input and output, sensor acquisition, filtering, registration, segmentation, surface reconstruction, recognition, and visualization.

PCL does not document a single persistent user configuration or credentials file; behavior is normally selected through library APIs, build options, or individual tool arguments.

为什么软件包爱好者会关心

PCL packages a broad 3D-perception stack—filters, registration, feature extraction, segmentation, reconstruction, recognition, visualization, and sensor input—behind reusable C++ libraries and a large collection of command-line tools.

Its modular libraries, substantial native dependency graph, optional acceleration and sensor backends, and many file-conversion utilities make PCL both unusually capable and unusually demanding to package consistently across platforms.

时间线

• 2010: Early PCL source copyrights identify Willow Garage as the originating organization.
• 2011: Radu Bogdan Rusu and Steve Cousins presented PCL at ICRA.
• 2012: Official source copyrights begin identifying Open Perception stewardship.

Related projects

• ROS developers are identified by the official project wiki as major PCL users.
• OpenNI and several depth-camera and LiDAR interfaces feed sensor data into PCL workflows.
• Eigen, Boost, FLANN, and VTK form important parts of PCL's numerical, search, portability, and visualization ecosystem.

来源

• https://github.com/PointCloudLibrary/pcl#readme
• https://pointclouds.org/assets/pdf/pcl_icra2011.pdf
• https://pointclouds.org/documentation/
• https://pointclouds.org/documentation/tutorials/