Raycasting of Light Field Galleries from Volumetric Data

Rezk‐Salama, Christof; Todt, Severin; Kolb, Andreas

doi:10.1111/j.1467-8659.2008.01215.x

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…In this way, ghosting artefacts are minimized. In addition, this light field representation and rendering technique has been extended with progressive data transfer and level‐of‐detail techniques [TRSBK08], and it has been applied to interactive high‐quality rendering in various application areas [RSTK08].…”

Section: Light Fieldsmentioning

confidence: 99%

Time‐of‐Flight Cameras in Computer Graphics

Kolb

Barth

Koch

et al. 2010

Computer Graphics Forum

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322

228

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A growing number of applications depend on accurate and fast 3D scene analysis. Examples are model and lightfield acquisition, collision prevention, mixed reality and gesture recognition. The estimation of a range map by image analysis or laser scan techniques is still a time-consuming and expensive part of such systems. A lower-priced, fast and robust alternative for distance measurements are time-of-flight (ToF) cameras. Recently, significant advances have been made in producing low-cost and compact ToF devices, which have the potential to revolutionize many fields of research, including computer graphics, computer vision and human machine interaction (HMI).These technologies are starting to have an impact on research and commercial applications. The upcoming generation of ToF sensors, however, will be even more powerful and will have the potential to become 'ubiquitous real-time geometry devices' for gaming, web-conferencing, and numerous other applications. This paper gives an account of recent developments in ToF technology and discusses the current state of the integration of this technology into various graphics-related applications.

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Section: Light Fieldsmentioning

confidence: 99%

Time‐of‐Flight Cameras in Computer Graphics

Kolb

Barth

Koch

et al. 2010

Computer Graphics Forum

Self Cite

322

228

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“…Since light‐field rendering is a low‐cost image‐based technique, it has been used in combination with volume rendering before. Approaches such as that presented in [RSTK08] convert large volumetric datasets into a light‐field representation in an offline preprocessing step. The light‐field data is then used for rendering the volumetric content at interactive frame rates that cannot be supported by online volume rendering.…”

Section: Related Workmentioning

confidence: 99%

Rendering Gigaray Light Fields

Birklbauer

Opelt

Bimber

2013

Computer Graphics Forum

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Figure 1: A 2.54 gigaray, 360 • panoramic light field (spatial resolution: 17,885×1,260 pixels, angular resolution: 11×11, 7.61 GB) at two different focus settings (top: far; center: near), and close-ups in native resolution (bottom), rendered at 8-43 fps (full aperture -smallest aperture, at a render resolution of 1280×720) using off-the-shelf graphics hardware. AbstractWe present a caching framework with a novel probability-based prefetching and eviction strategy applied to atomic cache units that enables interactive rendering of gigaray light fields. Further, we describe two new use cases that are supported by our framework: panoramic light fields, including a robust imaging technique and an appropriate parameterization scheme for real-time rendering and caching; and light-field-cached volume rendering, which supports interactive exploration of large volumetric datasets using light-field rendering. We consider applications such as light-field photography and the visualization of large image stacks from modern scanning microscopes.

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“…A good basis for comparison would be to compare image/video quality, at a fixed framerate, for different rendering techniques. Techniques, such as that of Rezk-Salama et al [44], for generating high-quality light field representations from geometric/volumetric data provide a basis for undertaking such an investigation.…”

Section: Future Workmentioning

confidence: 99%

“…Techniques, such as that of Rezk‐Salama et al . , for generating high‐quality light field representations from geometric/volumetric data provide a basis for undertaking such an investigation.…”

Section: Future Workmentioning

confidence: 99%

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On‐demand transmission model for remote visualization using image‐based rendering

Al-Saidi

Walker

Rana

2012

Concurrency and Computation

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Interactive distributed visualization is an emerging technology with numerous applications. However, many of the present approaches to interactive distributed visualization have limited performance because they are based on the traditional polygonal processing graphics pipeline. In contrast, image-based rendering uses multiple images of the scene instead of a three-dimensional geometrical representation, and so has the key advantage that the final output is independent of the scene complexity and depends on the desired final image resolution. Furthermore, the discrete nature of the light field dataset maps well to a hybrid solution, which can overcome the identified drawbacks. In this paper, we propose an on-demand solution for efficiently transmitting visualization data to remote users/clients. This is achieved through sending selected parts of the dataset based on the current client viewpoint, and is done instead of downloading a complete replica of the light field dataset to each client, or remotely sending a single rendered view back from a central server to the user each time the user updates their viewing parameters. The on-demand approach shows stable performance as the number of clients increases because the load on the server and the network traffic are reduced. Furthermore, detailed performance studies show that the proposed scheme outperforms the current solution in terms of interactivity measured in frames per second.ON-DEMAND TRANS. MODEL USING IBR FOR REMOTE VIZ 2329 viewpoint, two key challenges must be met and overcome. First, to ensure smooth navigation, the distributed visualization system must be capable of delivering between 10-15 frames/s to each user connected to the system [1]. Such a high interactive frame rate is generally difficult to achieve for systems with low to mid range rendering hardware. The second issue is the limitation of the network resources, such as low bandwidth and high latency. In order for distributed visualization systems to work efficiently under conditions of high user interactivity, which places a heavy load on the communication infrastructure, careful performance characterization and tuning are required. Current visualization systems require a traditional graphics pipeline, which uses three-dimensional (3D) geometrical rendering to produce a two-dimensional (2D) frame buffer, which is then sent over the network. Once datasets become sufficiently complex, the number of polygons is too large to fit into the memory of a Graphics Processing Unit, and must be paged in from the main system memory. Once the dataset is larger than the local system memory, then these data must in turn be paged in from local disk. Furthermore, mobile devices, such as smart phones, are becoming more widely used. These devices have less memory and weaker processing capabilities than desktop and laptop computers, and cannot accommodate modern Graphics Processing Units because of chip size, cost and power consumption considerations. There is, therefore, a need to investigate ways to provide inte...

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Raycasting of Light Field Galleries from Volumetric Data

Cited by 10 publications

References 12 publications

Time‐of‐Flight Cameras in Computer Graphics

Time‐of‐Flight Cameras in Computer Graphics

Rendering Gigaray Light Fields

On‐demand transmission model for remote visualization using image‐based rendering

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