Real-time compression of point cloud streams

Julius Kammerl, Nico Blodow, Radu Bogdan Rusu, Suat Gedikli, Michael Beetz, Eckehard Steinbach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

319 Scopus citations

Abstract

We present a novel lossy compression approach for point cloud streams which exploits spatial and temporal redundancy within the point data. Our proposed compression framework can handle general point cloud streams of arbitrary and varying size, point order and point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the point clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive point clouds. By encoding their structural differences, we can successively extend the point clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the point cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages778-785
Number of pages8
ISBN (Print)9781467314039
DOIs
StatePublished - 2012
Event 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 - Saint Paul, MN, United States
Duration: 14 May 201218 May 2012

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Country/TerritoryUnited States
CitySaint Paul, MN
Period14/05/1218/05/12

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