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Previous approaches to rendering large point clouds on immersive displays have generally created a trade-off between interactivity and quality. While these approaches have been quite successful for desktop environments when interaction is limited, virtual reality systems are continuously interactive, which forces users to suffer through either low frame rates or low image quality. This paper presents a novel approach to this problem through a progressive feedback-driven rendering algorithm. This algorithm uses reprojections of past views to accelerate the reconstruction of the current view. The presented method is tested against previous methods, showing improvements in both rendering quality and interactivity.

Simulations, such as n-body or smoothed particle hydrodynamics, create large amounts of time variant data that can generally only be visualized non-interactively by rendering the resulting interaction into movies from a fixed viewpoint. This is due to the large amount of unstructured data that make these data incapable of either loading into graphics memory or streaming from disk. The presented approach aims to preserve the interactively navigable 3D structure of the underlying data by reordering and compressing the data in such a way as to optimize playback rates. Additionally, the presented method is able to detect and describe coherent group motion between frames which might be used for analysis.

This document introduces a new application for rendering massive LiDAR point cloud data sets of interior environments within highresolution immersive VR display systems. Overall contributions are: to create an application which is able to visualize large-scale point clouds at interactive rates in immersive display environments, to develop a flexible pipeline for processing LiDAR data sets that allows display of both minimally processed and more rigorously processed point clouds, and to provide visualization mechanisms that produce accurate rendering of interior environments to better understand physical aspects of interior spaces. The work introduces three problems with producing accurate immersive rendering of Li-DAR point cloud data sets of interiors and presents solutions to these problems. Rendering performance is compared between the developed application and a previous immersive LiDAR viewer.

Ray tracing is an elegant and intuitive image generation method. The introduction of GPU-accelerated ray tracing and corresponding software frameworks makes this rendering technique a viable option for Augmented Reality applications. Spatial Augmented Reality employs projectors to illuminate physical models and is used in fields that require photorealism, such as design and prototyping. Ray tracing can be used to great effect in this Augmented Reality environment to create scenes of high visual fidelity. However, the peculiarities of SAR systems require that core ray tracing algorithms be adapted to this new rendering environment. This paper highlights the problems involved in using ray tracing in a SAR environment and provides solutions to overcome them. In particular, the following issues are addressed: ray generation, hybrid rendering and view-dependent rendering. Refereed/Peer-reviewed