Point Cloud Compression And Low Latency Streaming
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Point Cloud Compression and Low Latency Streaming
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.
Virtual Reality and Mixed Reality
This book constitutes the refereed proceedings of the 19th International Conference on Virtual Reality and Mixed Reality, EuroXR 2022, held in Stuttgart, Germany, in September 2022. The 6 full and 2 short papers were carefully reviewed and selected from 37 submissions. The conference presents contributions on results and insights in Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), commonly referred to under the umbrella of Extended Reality (XR), including software systems, immersive rendering technologies, 3D user interfaces, and applications.
The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue
The DARPA Robotics Challenge was a robotics competition that took place in Pomona, California USA in June 2015. The competition was the culmination of 33 months of demanding work by 23 teams and required humanoid robots to perform challenging locomotion and manipulation tasks in a mock disaster site. The challenge was conceived as a response to the Japanese Fukushima nuclear disaster of March 2011. The Fukushima disaster was seen as an ideal candidate for robotic intervention since the risk of exposure to radiation prevented human responders from accessing the site. This volume, edited by Matthew Spenko, Stephen Buerger, and Karl Iagnemma, includes commentary by the organizers, overall analysis of the results, and documentation of the technical efforts of 15 competing teams. The book provides an important record of the successes and failures involved in the DARPA Robotics Challenge and provides guidance for future needs to be addressed by policy makers, funding agencies, and the robotics research community. Many of the papers in this volume were initially published in a series of special issues of the Journal of Field Robotics. We have proudly collected versions of those papers in this STAR volume.