Compressing ROS Sensor and Geometry Messages with Draco

Wiemann, Thomas; Igelbrink, Felix; Pütz, Sebastian; Piening, Malte Kleine; Schupp, Steffen; Hinderink, Steffen; Vana, Juri; Hertzberg, Joachim

doi:10.1109/ssrr.2019.8848965

Cited by 7 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Draco aims to improve the storage and transmission of 3D graphics by compressing meshes and point-cloud data. The huge impact of Draco is depicted in current bibliography, as it influences and drives the design of alternative compression/decompression techniques [52], [53], [54], [55], [56], [57], [58], [59], [60], [61]. The framework uses a kd-tree to efficiently store data corresponding to points, connectivity information, texture coordinates, color information, normals and any other generic attributes associated with geometry.…”

Section: Dracomentioning

confidence: 99%

Cloud-based XR Services: A Survey on Relevant Challenges and Enabling Technologies

Θεοδωρόπουλος¹,

Makris²,

Taleb³

et al. 2022

J-NaNA

View full text Add to dashboard Cite

In recent years, the emergence of XR (eXtended Reality) applications, including Holography, Augmented, Virtual and Mixed Reality, has resulted in the creation of rather demanding requirements for Quality of Experience (QoE) and Quality of Service (QoS). In order to cope with requirements such as ultra-low latency and increased bandwidth, it is of paramount importance to leverage certain technological paradigms. The purpose of this paper is to identify these QoE and QoS requirements and then to provide an extensive survey on technologies that are able to facilitate the rather demanding requirements of Cloud-based XR Services. To that end, a wide range of enabling technologies are explored. These technologies include e.g. the ETSI (European Telecommunications Standards Institute) Multi-Access Edge Computing (MEC), Edge Storage, the ETSI Management and Orchestration (MANO), the ETSI Zero touch network & Service Management (ZSM), Deterministic Networking, the 3GPP (3rd Generation Partnership Project) Media Streaming, MPEG’s (Moving Picture Experts Group) Mixed and Augmented Reality standard, the Omnidirectional MediA Format (OMAF), ETSI’s Augmented Reality Framework etc.

show abstract

Section: Dracomentioning

confidence: 99%

Cloud-based XR Services: A Survey on Relevant Challenges and Enabling Technologies

Θεοδωρόπουλος¹,

Makris²,

Taleb³

et al. 2022

J-NaNA

View full text Add to dashboard Cite

show abstract

“…Draco is a compression algorithm developed by Google [29], which enables the compression of meshes and point cloud data [42]. Due to the differences between the two data representations, Draco does not rely on a single compression algorithm but uses multiple techniques to compress both representations on the basis of compression ratio, decoding speed, and discretization losses.…”

Section: B: Dracomentioning

confidence: 99%

Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices

et al. 2020

View full text Add to dashboard Cite

Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to detect people's mobility in smart monitoring. Due to the sheer volume of point cloud data, data transmission requires a significant amount of communication resources. In order to monitor people's mobility in real time, it is necessary to reduce the amount of transmission data to shorten delay. Point cloud compression is one method for reducing the amount of data. However, prior works addressing point cloud compression mainly focused on accuracy for the compression of an entire point cloud without considering its spatial characteristics. The more dynamically a spatial region changes, the more important it is when detecting moving objects such as cars, trucks, pedestrians, and bikes in smart monitoring. This paper proposes a prioritized transmission scheme that applies multiple point cloud compression methods to point cloud data according to the spatial importance of the data, i.e., how dynamically spatial regions change. This paper assumes data transmission of point cloud data from multiple LIDAR devices to an edge server and addresses the intra-frame geometry compression of point cloud data. The proposed scheme splits the point cloud into multiple classes according to the spatial importance and applies multiple point cloud compression methods to each class. A numerical study using a real point cloud dataset obtained at an intersection demonstrates the dependencies of quality, volume, and processing time on possible compression format options. The results verify that the proposed scheme reduces the amount of point cloud data drastically while satisfying the quality and processing time requirements.

show abstract