Content conditioning and distribution for dynamic virtual worlds

Metaverses are three-dimensional virtual worlds where anyone can add and script new objects. Metaverses today, such as Second Life, are dull, lifeless, and stagnant because users can see and interact with only a tiny region around them, rather than a large and immersive world. The next-generation Sirikata metaverse server scales to support large, complex worlds, even as it allows users to see and interact with the entire world. However, enabling large worlds poses a new challenge to graphical clients to display high-quality scenes quickly over a network. Arbitrary 3D content is often not optimized for real-time rendering, limiting the ability of clients to display large scenes consisting of hundreds or thousands of objects. We present the design and implementation of Sirikata's automatic, unsupervised conversion process that transforms 3D content into a format suitable for real-time rendering while minimizing loss of quality. The resulting progressive format includes a base mesh, allowing clients to quickly display the model, and a progressive portion for streaming additional detail as desired. 3D models are large—often several megabytes in size. This poses a challenge for online, interactive virtual worlds like Sirikata, where 3D content must be downloaded on-demand. When a client enters a scene containing many objects and the models are not cached locally on the client's device, it can take a long time to download, resulting in poor visual fidelity. Deciding how to order downloads has a huge impact on performance. Should a client download a higher texture resolution for one model, or stream additional vertices for another? Worse, underpowered clients might not be able to display a high resolution mesh, resulting in wasted time downloading unneeded content. Several metrics, such as the distance and scale of an object in the scene or the camera angle of the observer, can be taken into account when designing a scheduling algorithm. We present the design and implementation of a framework for evaluating scheduling algorithms for progressive meshes and we perform this evaluation on several independent metrics and methods for combining metrics, including a linear optimization algorithm. After a thorough evaluation, our results show that a simple metric—solid angle—consistently outperforms all other metrics.