Although there are many adaptive (or view-dependent) multiresolution methods, support for progressive transmission and reconstruction has not been addressed. A major reason for this is that most of these methods require a large portion of the hierarchical data structure to be available at the client before rendering starts. This is due to the dependency constraints among neighboring vertices. In this paper, we present an efficient, adaptive, multiresolution method that allows progressive and selective model transmission. It is achieved by reducing the neighboring dependency to a minimum. The new method allows visually important parts of an object to be transmitted to the client at higher priority than the less important parts, and progressively reconstructed there for display. It is even possible to transmit only the visible parts of a model and reconstruct these visible parts at the client. The ability to selectively transmit allows the visualization of very large models across the network with minimal delay. We will present how our method works in a client-server environment. We will also show the data structure of the transmission record and some performance results of the method.

Many multiresolution methods have been proposed. Most of them emphasize accuracy and hence are slow. Some methods may be fast, but they may not preserve the geometry of the model. Although there are a few real-time multiresolution methods available, they are developed mainly for handling large terrain models. In this paper, we present a very efficient multiresolution method for continuously reducing the resolution of a triangle model by incrementally removing triangles from it. The algorithm is simple to implement, requires no complicated data structures, and has a linear triangle deletion rate. We also present a method for caching the most recent sequence of triangle removal operations into a list, called the simplification list , so that it is possible to continuously increase the resolution of the model by inserting triangles in the reverse order of the sequence. We will compare our method with Hoppe's progressive meshes. Towards the end of the paper, we discuss the performance and memory usage of our method.