Complex renderings of synthetic scenes or virtual environments, once deemed impossible for consumer rendering, are becoming available as tools for young artists. These renderings, due to their high-quality image synthesis, can take minutes to hours to render. With the increase of the computing power, scientific simulation can produce datasets in the tera-bytes or even pita-bytes of range. Rendering one frame of these massive datasets sometimes can take a long time to finish. Therefore, interactivity for these large datasets and complex scenes is not possible using traditional rendering techniques. Our work focuses on using Image-Based Rendering (1BR) techniques to manage and explore large and complex datasets and virtual scenes on a remote display across the high-speed network. The key idea for this research is to pre-process the scene and render key viewpoints on pre-selected paths inside the scene. We then save these pre-processed partial results into a database on the server. The user can browse through the datasets on the client side. Whenever he or she needs information, the clients sends the request through the high speed network to the server and the server can retrieve the information and send it through the network. In this dissertation, we present new Image-based models to reconstruct approximations to any view along the path, which allows the user to roam around inside the virtual environments with interactive frame rates. We partition the scenes or datasets into several depth slabs to avoid occlusion and dis-occlusion problems. We then use mesh simplification methods to simplify the geometry produced from the pre-rendering step so that the underline geometry is manageable for our system. We compare two simplification methods, namely, down-sampling and feature preserved mesh simplification schemes and compare the errors produced by each methods. We then present the methods to manage our image-based database to achieve even more efficient renderings. These methods include empty texture tiles removal, texture bindings reduction technique, a novel two-part caching and pre-fetching scheme and our new data reduction method using a track-dependent occlusion culling algorithm. The system has been successfully tested on several scenes and on two different platforms and satisfying results have been obtained which proves that our system is suitable for handling large datasets and complex scenes.
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