Point Cloud Compression and Low Latency Streaming
Metadata[+] Show full item record
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.
Table of Contents
Introduction -- Background -- Experimental and computational details -- Conclusion -- Appendix