On‐demand transmission model for remote visualization using image‐based rendering

Abstract

Interactive distributed visualization is an emerging technology with numerous applications. However, many of the present approaches to interactive distributed visualization have limited performance because they are based on the traditional polygonal processing graphics pipeline. In contrast, image-based rendering uses multiple images of the scene instead of a three-dimensional geometrical representation, and so has the key advantage that the final output is independent of the scene complexity and depends on the desired final image resolution. Furthermore, the discrete nature of the light field dataset maps well to a hybrid solution, which can overcome the identified drawbacks. In this paper, we propose an on-demand solution for efficiently transmitting visualization data to remote users/clients. This is achieved through sending selected parts of the dataset based on the current client viewpoint, and is done instead of downloading a complete replica of the light field dataset to each client, or remotely sending a single rendered view back from a central server to the user each time the user updates their viewing parameters. The on-demand approach shows stable performance as the number of clients increases because the load on the server and the network traffic are reduced. Furthermore, detailed performance studies show that the proposed scheme outperforms the current solution in terms of interactivity measured in frames per second.ON-DEMAND TRANS. MODEL USING IBR FOR REMOTE VIZ 2329 viewpoint, two key challenges must be met and overcome. First, to ensure smooth navigation, the distributed visualization system must be capable of delivering between 10-15 frames/s to each user connected to the system [1]. Such a high interactive frame rate is generally difficult to achieve for systems with low to mid range rendering hardware. The second issue is the limitation of the network resources, such as low bandwidth and high latency. In order for distributed visualization systems to work efficiently under conditions of high user interactivity, which places a heavy load on the communication infrastructure, careful performance characterization and tuning are required. Current visualization systems require a traditional graphics pipeline, which uses three-dimensional (3D) geometrical rendering to produce a two-dimensional (2D) frame buffer, which is then sent over the network. Once datasets become sufficiently complex, the number of polygons is too large to fit into the memory of a Graphics Processing Unit, and must be paged in from the main system memory. Once the dataset is larger than the local system memory, then these data must in turn be paged in from local disk. Furthermore, mobile devices, such as smart phones, are becoming more widely used. These devices have less memory and weaker processing capabilities than desktop and laptop computers, and cannot accommodate modern Graphics Processing Units because of chip size, cost and power consumption considerations. There is, therefore, a need to investigate ways to provide inte...

Automatic cloud resource management for interactive remote geovisualization

“…Remote visualization has been considered as a plausible solution for ultra‐scale geospatial data analysis if the rendering of massive data cannot be performed locally due to overwhelming data size and limited local hardware capabilities (Lamberti & Sanna, ). In the literature, attention has centered on the development of various remote visualization tools (Henderson, ; Jourdain, Ayachit, & Geveci, ; Williams et al, ) and related technologies when building such tools (e.g., caching, prefetching, image composition, data compression, and deployment) (Sisneros et al, ; Al‐Saidi, Walker, & Rana, ; Cui, Ma, & Popescu, ; Quax et al, ). These developments mostly rely on specific supercomputers or large‐scale clusters to perform remote data visualization.…”