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a b s t r a c tA room-sized, walk-in virtual reality (VR) display is to a typical computer screen what a supercomputer is to a laptop computer. It is a vastly more complex system to design, house, optimize, make usable, and maintain. 17 years of designing and implementing room-sized ''CAVE'' VR systems have led to significant new advances in visual and audio fidelity. CAVEs are a challenge to construct because their hundreds of constituent components are mostly adapted off-the-shelf technologies that were designed for other uses. The integration of these components and the building of certain critical custom parts like screens involve years of research and development for each new generation of CAVEs. The difficult issues and compromises achieved and deemed acceptable are of keen interest to the relatively small community of VR experimentalists, but also may be enlightening to a broader group of computer scientists not familiar with the barriers to implementing virtual reality and the technical reasons these barriers exist.The StarCAVE, a 3rd-generation CAVE, is a 5-wall plus floor projected virtual reality room, operating at a combined resolution of ∼68 million pixels, ∼34 million pixels per eye, distributed over 15 rear- • to increase immersion, while reducing stereo ghosting. The non-depolarizing, wear-resistant floor screens are lit from overhead. Digital audio sonification is achieved using surround speakers and wave field synthesis, while user interaction is provided via a wand and multi-camera, wireless tracking system.

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The CAVE, a walk-in virtual reality environment typically consisting of 4–6 3 m-by-3 m sides of a room made of rear-projected screens, was first conceived and built in 1991. In the nearly two decades since its conception, the supporting technology has improved so that current CAVEs are much brighter, at much higher resolution, and have dramatically improved graphics performance. However, rear-projection-based CAVEs typically must be housed in a 10 m-by-10 m-by-10 m room (allowing space behind the screen walls for the projectors), which limits their deployment to large spaces. The CAVE of the future will be made of tessellated panel displays, eliminating the projection distance, but the implementation of such displays is challenging. Early multi-tile, panel-based, virtual-reality displays have been designed, prototyped, and built for the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia by researchers at the University of California, San Diego, and the University of Illinois at Chicago. New means of image generation and control are considered key contributions to the future viability of the CAVE as a virtual-reality device.

The Cross Platform Cluster Graphics Library (CGLX) is a flexible and transparent OpenGL-based graphics framework for distributed, high-performance visualization systems. CGLX allows OpenGL based applications to utilize massively scalable visualization clusters such as multiprojector or high-resolution tiled display environments and to maximize the achievable performance and resolution. The framework features a programming interface for hardware-accelerated rendering of OpenGL applications on visualization clusters, mimicking a GLUT-like (OpenGL-Utility-Toolkit) interface to enable smooth translation of single-node applications to distributed parallel rendering applications. CGLX provides a unified, scalable, distributed OpenGL context to the user by intercepting and manipulating certain OpenGL directives. CGLX's interception mechanism, in combination with the core functionality for users to register callbacks, enables this framework to manage a visualization grid without additional implementation requirements to the user. Although CGLX grants access to its core engine, allowing users to change its default behavior, general development can occur in the context of a standalone desktop. The framework provides an easy-to-use graphical user interface (GUI) and tools to test, setup, and configure a visualization cluster. This paper describes CGLX's architecture, tools, and systems components. We present performance and scalability tests with different types of applications, and we compare the results with a Chromium-based approach.

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Diffusion Tensor Imaging (DTI) of the human brain, coupled with tractography techniques, enable the extraction of large-collections of three-dimensional tract pathways per subject. These pathways and pathway bundles represent the connectivity between different brain regions and are critical for the understanding of brain related diseases. A flexible and efficient GPU-based rendering technique for DTI tractography data is presented that addresses common performance bottlenecks and image-quality issues, allowing interactive render rates to be achieved on commodity hardware. An occlusion query-based pathway LoD management system for streamlines/streamtubes/tuboids is introduced that optimizes input geometry, vertex processing, and fragment processing loads, and helps reduce overdraw. The tuboid, a fully-shaded streamtube impostor constructed entirely on the GPU from streamline vertices, is also introduced. Unlike full streamtubes and other impostor constructs, tuboids require little to no preprocessing or extra space over the original streamline data. The supported fragment processing levels of detail range from texture-based draft shading to full raycast normal computation, Phong shading, environment mapping, and curvature-correct text labeling. The presented text labeling technique for tuboids provides adaptive, aesthetically pleasing labels that appear attached to the surface of the tubes. Furthermore, an occlusion query aggregating and scheduling scheme for tuboids is described that reduces the query overhead. Results for a tractography dataset are presented, and demonstrate that LoD-managed tuboids offer benefits over traditional streamtubes both in performance and appearance.