Abstract
The complexity in 3D virtual environment over the web is growing rapidly every day. This 3D virtual environment comprises a set of structured scenes and each scene has multiple 3D objects/meshes. Therefore the granular level of the block in a virtual environment is the object. In a virtual environment, it is required to give user interactions for every 3D object and at any point of time, it is enough if the system streams and brings in only the visible portion of the object from the server to the client by utilizing the limited network bandwidth and the limited client memory space. This streaming would reduce the time to present the rendered object to the requested clients. Further to reduce the time and effectively utilize the bandwidth and memory space, in the proposed study, an attempt is made to exploit the user interaction on 3D object and built a predictive agent which would minimize the latency in the rendering of the 3D mesh that is being streamed. The experiment result shows that the rendering time and cache miss rates are significantly reduced with the predictive agent.
Highlights
In recent times, 3D modeling and rendering has gained attention over the internet and most of the multiuser virtual environment renders the entire world once it is fully downloaded from the server
Previous history collated from various user inputs, the set of predicted vertices and faces are pushed to the client with the help of the Predictive Agent (PA)
Server agent: The Server Agent receives the client input and output from the visibility culler and store into the dynamic data structure with the reference bit is set against profiling analysis carried out offline by collating the user interactions of 50 users across various models, the key press is predicted and the corresponding 3D
Summary
3D modeling and rendering has gained attention over the internet and most of the multiuser virtual environment renders the entire world once it is fully downloaded from the server. The PA contains the typical key press and patterns of all users which will be used further to predict their navigation This in turn helps to optimize the 3D streaming and rendering over web by reducing the time delay between user request and response. View dependent mesh streaming with minimal latency: The study proposed by (Kim, et al, 2004) This would reduce the time delay between the user request and response. The central idea is to predict the user navigation and construct an analytical model for every 3D object (3D meshes) using the PA This predictive model would be useful in bringing the necessary surfaces during streaming so that rendering and response time can be reduced.
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