dc.contributor.advisor | Zhu, Li | |
dc.contributor.author | Ainala, Karthik | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017 Fall | |
dc.description | Title from PDF of title page viewed May 16, 2018 | |
dc.description | Thesis advisor: Zhu Li | |
dc.description | Vita | |
dc.description | Includes bibliographical references (page 25-26) | |
dc.description | Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017 | |
dc.description.abstract | With the commoditization of the 3D depth sensors, we can now very easily model
real objects and scenes into digital domain which then can be used for variety of application
in gaming, animation, virtual reality, immersive communication etc. Modern sensors are
capable of capturing objects with very high detail and scene of large area and thus might
include millions of points. These point data usually occupy large storage space or require
high bandwidth in case of real-time transmission. Thus, an efficient compression of these
huge point cloud data points becomes necessary. Point clouds are often organized and
compressed with octree based structures. The octree subdivision sequence is often serialized
in a sequence of bytes that are subsequently entropy encoded using range coding, arithmetic
coding or other methods. Such octree based algorithms are efficient only up to a certain level
of detail as they have an exponential run-time in the number of subdivision levels. In
addition, the compression efficiency diminishes when the number of subdivision levels
increases. In this work we present an alternative way to partition the point cloud data. The
point cloud is divided based on the data partition using kd tree binary division instead of
Octree’s space partition method and forming a base layer. In base layer leaf nodes, the
distribution of points is considered and projected to a 2D plane based on the flatness of the
node points. Octree and Quadtree based partition is used to further convert the data to
bitstreams. These are scalable point cloud bitstreams as we need only specific number of kd
nodes in each time for a specific point of view. The use case is navigation in autonomous
vehicles where it requires point cloud information up to a specific distance at different
speeds. These scalable bitstreams of kd nodes can be used in real time transmission with low
latency. Results show that compression performance is improved for geometry compression
in point clouds and a scalable low latency streaming model is shown for navigation use case. | eng |
dc.description.tableofcontents | Introduction -- Background -- Experimental and computational details -- Conclusion -- Appendix | |
dc.format.extent | ix, 27 pages | |
dc.identifier.uri | https://hdl.handle.net/10355/63254 | |
dc.publisher | University of Missouri--Kansas City | eng |
dc.subject.lcsh | Three-dimensional modeling | |
dc.subject.lcsh | Data compression (Computer science) | |
dc.subject.other | Thesis -- University of Missouri--Kansas City -- Engineering | |
dc.title | Point Cloud Compression and Low Latency Streaming | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Electrical Engineering (UMKC) | |
thesis.degree.grantor | University of Missouri--Kansas City | |
thesis.degree.level | Masters | |
thesis.degree.name | M.S. | |