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

Interactive distributed visualization is an emerging technology with numerous applications. However, many of the present approaches to interactive distributed visualization have limited performance since they are based on the traditional polygonal processing graphics pipeline. In contrast, image-based rendering uses multiple images of the scene instead of a 3D 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.