3D triangular mesh is becoming an increasingly important data type for networked applications such as digital museums, online games, and virtual worlds. In these applications, a multi-resolution representation is typically desired for streaming large 3D meshes, allowing for incremental rendering at the viewers while data is still being transmitted. Such progressive coding, however, introduces dependencies between data. This paper quantitatively analyzes the effects of such dependency on the intermediate decoded mesh quality when the progressive mesh is transmitted over a lossy network, by modeling the distribution of decoding time as a function of mesh properties and network parameters. To illustrate the usefulness of our analytical model, we describe three of its applications. First, we show how it can be used to analytically compute the expected decoded mesh quality. Second, we study two extreme cases of dependency in progressive mesh and show that the effect of dependencies on decoded mesh quality diminishes with time. Finally, based on the model, we propose a packetization strategy that improves the decoded mesh quality during the initial stage of streaming.

Progressive mesh streaming enables users to view 3D meshes over the network with increasing level of details, by sending coarse version of the meshes initially, followed by a series of refinements. To optimally increase the rendered mesh quality, refinements should be sent in descending order of their visual contributions based on the user's viewpoint. A common approach is to let the sender decide this sending order, but the computational cost of making this decision prohibits such sender-driven approach from scaling to large number of clients. To improve scalability, we propose a receiver-driven protocol, in which the receiver decides the sending order and explicitly requests the refinements, while the sender simply sends the data requested. The sending order is computed at the receiver by estimating the visibility and visual contributions of the refinements, even before receiving them, with the help of GPU. Experiments show that our protocol reduces the CPU cost of the sender by 24% and the outgoing traffic of the sender by 40%.

No abstract

Just as in the real world, plants are important objects in virtual world for creating pleasant and realistic environments, especially those involving natural scenes. As such, much effort has been made in realistic modeling of plants. As the trend moves towards networked and distributed virtual environment, however, the current models are inadequate as they are not designed for progressive transmissions. In this paper, we fill in this gap by proposing a progressive representation for plants based on generalized cylinders. To facilitate the transmission of the plants, we quantify the visual contribution of each branch and use this weight in packet scheduling. We show the efficiency of our representations and effectiveness of our packet scheduler through simulations.

3D triangular meshes are becoming an increasingly prevalent data type in networked applications such as digital museums, online games, and virtual worlds. In these applications, a 3D mesh is typically coded progressively, yielding a multiresolution representation suitable for streaming. While such progressive coding allows incremental rendering for users while data is being transmitted, it introduces dependencies between data, causing delay in rendering when packets are lost. This article quantitatively analyzes the effects of such dependency by modeling the distribution of decoding time as a function of mesh properties and network parameters. We apply our model to study two extreme cases of dependency in progressive meshes and show that the effect of dependencies on decoded mesh quality diminishes with time. Our model provides the expected decoded mesh quality at the receiver at a given time. Based on this expected value, we propose a packetization strategy that improves the decoded mesh quality during the initial stage of streaming. We validate the accuracy of our model under a variety of network conditions, including bursty losses, fluctuating RTT, and varying sending rate. The values predicted from our model match the measured value reasonably well in all cases except when losses are too bursty.