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CUDA 4.1 released

NVIDIA has posted a new release of the CUDA Toolkit. Of note is a significant enhancement to the NVIDIA Performance Primitives (NPP) library, a collection of GPU-accelerated image, video and signal processing functions that deliver 5x to 10x faster performance than comparable CPU-only implementations.

Using NPP, developers can take advantage of over 2000 image processing and signal processing primitives to achieve significant improvements in application performance. Whether you are simply replacing CPU primitives with GPU-accelerated versions or integrating NPP primitives with your existing GPU-accelerated pipeline, NPP delivers high performance while reducing development time. We have already seen speedups in PCL trunk by compiling and using our Kinect Fusion implementation as well as other GPU code with CUDA 4.1.

Other new features in CUDA 4.1 include:

  • 10% performance improvement with new LLVM-based CUDA compiler.
  • Re-designed Visual Profiler to give you step-by-step performance optimizations.

Find out more at

CUDA 4.1

PCL and Urban Robotics Code Sprint 2012

It is our immense pleasure to announce the beginning of a new PCL Code Sprint sponsored by Urban Robotics: PCL-URCS!

PCL Code Sprints are intended to rapidly advance the capabilities of the Point Cloud Library in a certain area/subject by offering stipends to talented student developers and pairing them with knowledgeable mentors for several months of accelerated software development. These sprints have been inspired by the Google Summer Of Code (GSOC) initiative, and we will be following the same basic model. Projects will run for an initial period of 3 months with the same structure and performance evaluations as the GSOC program, and all of the code produced will be open source.

For this winter's PCL-Urban Robotics Code Sprint, we have identified the following important area for further development in PCL, and we are therefore searching for outstanding candidates (and mentors) to work on the following project:

  1. Out-of-core octree integration: integrate and extend Urban Robotics's highly scalable, spatially searchable and colorized 3D octree-based point cloud format in PCL, to efficiently support the visualization and processing…

Tracking 3D objects with Point Cloud Library

Tracking 3D objects in continuous point cloud data sequences is an important research topic for mobile robots: it allows robots to monitor the environment and make decisions and adapt their motions according to the changes in the world. An example of such a typical application is visual servoing, with its key challenge to estimate the three dimensional pose of an object in real-time.

During his internship at Willow Garage, Ryohei Ueda from the JSK laboratory at University of Tokyo, worked on a novel 3D tracking library for the Point Cloud Library (PCL) project. The purpose of the library is to provide a comprehensive algorithmic base for the estimation of 3D object poses using Monte Carlo sampling techniques and for calculating the likelihood using combined weighted metrics for hyper-dimensional spaces including Cartesian data, colors, and surface normals. The libpcl_tracking library is optimized to perform computations in real-time, by employing multi CPU cores optimization, adaptive particle filtering (KLD sampling) and other modern techniques.

To find out more about Ryohei's…

PCL-TRCS kickstart!

PCL-TRCS is ready to start! Due to the extreme level of interest, we have received an impressive number of great applications, and therefore have decided to bump the number of projects/slots to 8. It is therefore our pleasure to announce the dream team that will be working on TRCS for the next 3-6 months!

PCL 1.4.0!

We're thrilled to announce that Point Cloud Library (PCL) version 1.4.0 has been released!

PCL 1.4

You can find the latest source and binaries on our download page:

Thanks to the hard work of all of our awesome contributors, we've got a big list of new features. The most notable overall changes are:


  • added float union to the pointXYZRGBL type, in order to aid PCD_viewer compatibility
  • bugfix: pcl::computeCovarianceMatrixNormalized did not normalize for each implementation
  • fixed bug #421: at(u,v) should return a const reference
  • added…

Augmented Reality with Kinect Fusion

When we open sourced the Kinect Fusion algorithm a few weeks ago, we were not expecting so many people to join our community and start helping co-develop and improve the system. As we said in our initial announcement, the new Kinect Fusion implementation was still experimental, and it still has a lot of rough edges. However, that didn't scare off Weipeng Xu, one of our fearless new users. He saw the potential of this code and quickly integrated it with the XNA framework, creating a foundation to support the development of future augmented reality games and applications.

Augmented Reality is a challenging application domain, and there are a number of difficult tasks that must be performed in any AR system. Notably, for many applications, it's necessary to be able to construct a good 3D model of the environment and accurately track the movements of the camera in real time. Weifeng was able to use a Kinect 3D camera and PCL's experimental implementation of the KinectFusion algorithm to solve both of these problems. The result, as shown in the videos below, is a markerless AR system with the ability to handle occlusion between the real scene and virtual objects.

JediBot powered by PCL

JediBot is an autonomous robot designed to duel with humans in simulated sword fights. It was created by a group of four students in Stanford University's Experimental Robotics class, who combined a KUKA Light-Weight Robot arm for manipulation and a Microsoft Kinect for 3D sensing, along with custom software that leverages several open source libraries, including the Point Cloud Library (PCL) for 3D perception, the Reflexxes Motion Library for motion planning, and the Fast Research Interface Library for low-level control.

When facing off against a human combatant, the JediBot's software starts by analyzing the point cloud data acquired from its Microsoft Kinect 3D-camera. The Point Cloud Library is used to robustly find the pose of an opponent's sword in real time. The JediBot then quickly plans its response. Depending on the current strategy (attack or defense) an appropriate pose of the robot's sword is calculated. This pose is then sent to the Reflexxes Motion Library, which continuously plans a smooth sequence of arm motions. The resulting robot motions are directly sent to the Fast Research Interface Library, which provides low-level access to the…

Urban Robotics Extends Octree Format to PCL Open Source Community

Urban Robotics Inc., a leading provider of three-dimensional (3D) imaging sensors, software and algorithms, today announced it is making its highly scalable, spatially searchable and colorized 3D octree-based point cloud format available to the Point Cloud Library (PCL) community.

Willow Garage launched PCL in March 2011, to help accelerate 3D algorithmic work related to robotic applications. It is free for research and commercial use. The addition of Urban Robotics's software code to PCL lays the foundation for the creation of a standardized format for large-scale 3D applications. An example of such large-scale dataset representing a reconstruction of Mount St. Helens is shown in the video below.

Urban Robotics developed its octree-based format to efficiently store and manage point cloud data, and to address challenges related to the rapid processing of massive 3D images during daily operations.

"The main challenge with supported LAS and XYZ point cloud file formats is that…

An open source implementation of KinectFusion

We are happy to announce the development of a new open source implementation of KinectFusion, a simple system for 3D local mapping with an OpenNI-compatible camera. Below you can find the original SIGGRAPH video, together with a complete description of the algorithm presented in KinectFusion: Real-Time Dense Surface Mapping and Tracking.

For a quick comparison, here's a demonstration of our current implementation's capabilities:

The preliminary source code is currently available in our SVN repository's trunk in the CUDA/KinFu module. Since this code is still unreleased and under active development, we won't be able to provide support via our forums yet; however, advanced users are free to check out the code and give it a try. Be advised that this code relies heavily on the NVidia CUDA development libraries for GPU optimizations and will require a compatible GPU for best results.

Moving forward, we continue to refine and improve the system, and we are hoping to improve upon the original…

PCL 1.3.1!

PCL 1.3.1

A new version of PCL is ready! PCL 1.3.1 includes lots of bug-fixes, and you can check out the complete list here:

The source code is ready to download now from

and if you prefer binary installers, we'll have those posted on the downloads page sometime in the next few days.

And as always, a big thanks to all the users and developers who helped us track down and fix these bugs.

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