Abstract
Inspired by the cache model, a predictive agent is analytically constructed to determine the user navigation based on the patterns derived out of user profiles. The user profiling is derived based on the user interactions made by the diversified set of users over different 3D models. An attempt has been made to analyze how efficiently the prediction works to stream a 3D model based on the pre determined transition path generated out of the user profiles. The transition paths for various models are generated by exploiting the properties of Markov Chain model. The analytics collected from the transition paths affirm that the predictive agent lessens the rendering latency significantly. The rendering latency is lessened by streaming the required data well before it is requested from the server to the client. The streaming and rendering process with user interactions from client would stream and render only the visible portion of the 3D models while ensuring that there is no compromise on the visual quality of the objects. This paper mainly focuses on profiling the user interactions during the navigation of 3D meshes and analyses various outcome of it.
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Vani, V., Pradeep Kumar, R., Mohan, S. (2013). Profiling User Interactions of 3D Complex Meshes for Predictive Streaming and Rendering. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 221. Springer, India. https://doi.org/10.1007/978-81-322-0997-3_41
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DOI: https://doi.org/10.1007/978-81-322-0997-3_41
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