GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering

Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures, and does not scale well with the size of the models. We present a GPU-based progressive mesh simplification approach which enables the interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we develop a novel data structure to represent the progressive LOD mesh, and design a parallel mesh simplification algorithm towards GPU architecture. Second, we propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel mesh simplification algorithm and GPU-based streaming approach significantly improve the overall rendering performance

Accelerating Monte Carlo simulations with an NVIDIA® graphics processor

Modern graphics cards, commonly used in desktop computers, have evolved beyond a simple interface between processor and display to incorporate sophisticated calculation engines that can be applied to general purpose computing. The Monte Carlo algorithm for modelling photon transport in turbid media has been implemented on an NVIDIA® 8800gt graphics card using the CUDA toolkit. The Monte Carlo method relies on following the trajectory of millions of photons through the sample, often taking hours or days to complete. The graphics-processor implementation, processing roughly 110 million scattering events per second, was found to run more than 70 times faster than a similar, single-threaded implementation on a 2.67 GHz desktop computer

Adaptive Mesh Fluid Simulations on GPU

We describe an implementation of compressible inviscid fluid solvers with block-structured adaptive mesh refinement on Graphics Processing Units using NVIDIA's CUDA. We show that a class of high resolution shock capturing schemes can be mapped naturally on this architecture. Using the method of lines approach with the second order total variation diminishing Runge-Kutta time integration scheme, piecewise linear reconstruction, and a Harten-Lax-van Leer Riemann solver, we achieve an overall speedup of approximately 10 times faster execution on one graphics card as compared to a single core on the host computer. We attain this speedup in uniform grid runs as well as in problems with deep AMR hierarchies. Our framework can readily be applied to more general systems of conservation laws and extended to higher order shock capturing schemes. This is shown directly by an implementation of a magneto-hydrodynamic solver and comparing its performance to the pure hydrodynamic case. Finally, we also combined our CUDA parallel scheme with MPI to make the code run on GPU clusters. Close to ideal speedup is observed on up to four GPUs.Comment: Submitted to New Astronom

A predictive approach for a real-time remote visualization of large meshes

Déjà sur HALRemote access to large meshes is the subject of studies since several years. We propose in this paper a contribution to the problem of remote mesh viewing. We work on triangular meshes. After a study of existing methods of remote viewing, we propose a visualization approach based on a client-server architecture, in which almost all operations are performed on the server. Our approach includes three main steps: a first step of partitioning the original mesh, generating several fragments of the original mesh that can be supported by the supposed smaller Transfer Control Protocol (TCP) window size of the network, a second step called pre-simplification of the mesh partitioned, generating simplified models of fragments at different levels of detail, which aims to accelerate the visualization process when a client(that we also call remote user) requests a visualization of a specific area of interest, the final step involves the actual visualization of an area which interest the client, the latter having the possibility to visualize more accurately the area of interest, and less accurately the areas out of context. In this step, the reconstruction of the object taking into account the connectivity of fragments before simplifying a fragment is necessary.Pestiv-3D projec

Towards the 3D Web with Open Simulator

Continuing advances and reduced costs in computational power, graphics processors and network bandwidth have led to 3D immersive multi-user virtual worlds becoming increasingly accessible while offering an improved and engaging Quality of Experience. At the same time the functionality of the World Wide Web continues to expand alongside the computing infrastructure it runs on and pages can now routinely accommodate many forms of interactive multimedia components as standard features - streaming video for example. Inevitably there is an emerging expectation that the Web will expand further to incorporate immersive 3D environments. This is exciting because humans are well adapted to operating in 3D environments and it is challenging because existing software and skill sets are focused around competencies in 2D Web applications. Open Simulator (OpenSim) is a freely available open source tool-kit that empowers users to create and deploy their own 3D environments in the same way that anyone can create and deploy a Web site. Its characteristics can be seen as a set of references as to how the 3D Web could be instantiated. This paper describes experiments carried out with OpenSim to better understand network and system issues, and presents experience in using OpenSim to develop and deliver applications for education and cultural heritage. Evaluation is based upon observations of these applications in use and measurements of systems both in the lab and in the wild.Postprin

Foveated Video Streaming for Cloud Gaming

Good user experience with interactive cloud-based multimedia applications, such as cloud gaming and cloud-based VR, requires low end-to-end latency and large amounts of downstream network bandwidth at the same time. In this paper, we present a foveated video streaming system for cloud gaming. The system adapts video stream quality by adjusting the encoding parameters on the fly to match the player's gaze position. We conduct measurements with a prototype that we developed for a cloud gaming system in conjunction with eye tracker hardware. Evaluation results suggest that such foveated streaming can reduce bandwidth requirements by even more than 50% depending on parametrization of the foveated video coding and that it is feasible from the latency perspective.Comment: Submitted to: IEEE 19th International Workshop on Multimedia Signal Processin