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Compressing Point Clouds

Julius Kammerl from Technische Universitaet Muenchen, Munich, Germany spent his internship at Willow Garage working on the Point Cloud Library (PCL). To find out more, please watch the video above. You can also read the slides below (download pdf) for more technical details.

Robots such as the PR2 by Willow Garage employ depth sensors for acquiring information about the shape and geometry of their environment. These sensors discretely sample the three dimensional space with high spatial resolution and high update rate and therefore generate large point data sets. Once these so called point clouds have to be stored on the robot or transmitted over rate-limited communication channels, the interest in compressing this kind of data emerges and efficient algorithms for compressing and communicating point clouds become highly relevant. Further applications for point cloud compression can be found in the field of 3D television/conferencing.

In our work we compress the point distribution by performing a spatial decomposition based on octree data structures. Furthermore, by correlating and referencing the currently sampled sensor data to previously sensed and transmitted point cloud information, temporal redundancy can be detected and removed from the point cloud data stream. In this context, the detection of changes within the point data sets is of great importance. By subsequently analyzing and comparing the octree data structures of adjacent point clouds, spatial changes in point data can be extracted and used to successively extend the point clouds at the decoder. In addition, an entropy coder (range/arithmetic coder) is used for further removing redundancy from the signals to be transmitted/stored. For more information, please visit

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