Content Conditioning and Distribution for Dynamic Virtual Worlds

Abstract

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.