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Spatial Augmented Reality Book (free ebook)

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VIOSO smartprojecting

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The Caleydo Project

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Location - Computer Science Building (S3)

Location of the Institute for Computer Graphics: Computer Science Building (Science Park 3)

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Content

Research

The Institute of Computer Graphics carries out research in a modern field that has been coined "visual computing". Our core disciplines cover the imaging, processing, visualization and displaying of visual data. These are enabled by new fields and technologies, such as light fields, projector-camera systems, responsive optics, mobile computing, and visual analytics.

Please select one of the following topics or years for detailed information.















Light Fields

Panorama Light-Field Imaging


We present a novel approach to recording and computing panorama light fields. In contrast to previous methods that estimate panorama light fields from focal stacks or naive multi-perspective image stitching, our approach is the first that processes ray entries directly and does not require depth reconstruction or matching of image features. Arbitrarily complex scenes can therefore be captured while preserving correct occlusion boundaries, anisotropic reflections, refractions, and other light effects that go beyond diffuse reflections of Lambertian surfaces.

Birklbauer, C. and Bimber, O., Panorama Light-Field Imaging, In proceedings of Eurographics (Computer Graphics Forum), 33(2), 43-52, 2014

Coded Exposure HDR Light-Field Video Recording


Capturing exposure sequences to compute high dynamic range (HDR) images causes motion blur in cases of camera movement. This also applies to light-field cameras: frames rendered from multiple blurred HDR lightfield perspectives are also blurred. While the recording times of exposure sequences cannot be reduced for a single-sensor camera, we demonstrate how this can be achieved for a camera array. Thus, we decrease capturing time and reduce motion blur for HDR light-field video recording. Applying a spatio-temporal exposure pattern while capturing frames with a camera array reduces the overall recording time and enables the estimation of camera movement within one light-field video frame. By estimating depth maps and local point spread functions (PSFs) from multiple perspectives with the same exposure, regional motion deblurring can be supported. Missing exposures at various perspectives are then interpolated.

Schedl, D. C., Birklbauer, C. and Bimber, O., Coded Exposure HDR Light-Field Video Recording, In proceedings of Eurographics (Computer Graphics Forum), 33(2), 33-42, 2014

  • application/pdfManuscript (41.7 MB)
  • Coded Exposure HDR Light-Field Video Recording


    Capturing exposure sequences for computing HDR images is prone to motion blur, which also affects HDR light-field recording.
    We record four exposures encoded at varying camera perspectives and deblur long exposure recordings by tracking features in low exposure recordings.
    This reduces motion blur and leads to shorter recording interval.

    Schedl, D. C., Birklbauer, C. and Bimber, O., Coded Exposure HDR Light-Field Video Recording, ACM Siggraph (poster+talk), 2013

    Rendering Gigaray Light Fields


    We 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.

    Birklbauer, C., Opelt, S. and Bimber, O., Rendering Gigaray Light Fields, In proceedings of Eurographics (Computer Graphics Forum), 32(2), 469-478, 2013

    Panorama Light-Field Imaging


    With increasing resolution of imaging sensors, light-field photography is now becoming increasingly practical, and first light-field cameras are already commercially available (e.g., Lytro, Raytrix, and others). Applying common digital image processing techniques to light-fields, however, is in many cases not straight forward. The reason for this is, that the outcome must not only be spatially consistent, but also directionally consistent. Otherwise, refocussing and perspective changes will cause strong image artifacts. Panorama imaging techniques, for example, are an integral part of digital photography – often being supported by camera hardware today. We present a first approach towards the construction of panorama light-fields (i.e., large field-of-view light-fields computed from overlapping sub-light-field recordings).
    By converting overlapping sub-light-fields into individual focal stacks, computing a panorama focal stack from them, and converting the panorama focal stack back into a panorama light field, we avoid the demand for a precise reconstruction of scene depth.

    Birklbauer, C. and Bimber, O., Panorama Light-Field Imaging, ACM Siggraph (poster+talk), 2012

    Light-Field Supported Fast Volume Rendering


    Advances in imaging technology leads to a continues increase of image data sets. Modern scanning microscopes, for instance, produce image stacks with a megapixel lateral resolution and many hundreds to thousands slices in axial direction. This trend will continue – resulting in very large volumetric data sets that are difficult to explore interactively, since the complexity of volume rendering is proportional to spatial and lateral resolution of the data. Light-field rendering is a fast and simple image-based rendering method that requires pre-computed image data. For volume rendering, each costly computed image is discarded after changing the viewing parameters, while it becomes idle if the viewing parameters are not changed and the visualization does not need to be updated. We combine light-field rendering and volume rendering with two goals: We recycle previously rendered images and use the idle times for filling a cached-managed light field. The final images are then composed from light-field rendering and from volume rendering – depending on the state of the light-field cache. This leads to a significant increase in rendering performance and to the ability of exploring large volumetric datasets interactively.

    Birklbauer, C. and Bimber, O., Light-Field Supported Fast Volume Rendering, ACM Siggraph (poster), 2012

    Light-Field Retargeting


    We present a first approach to light-field retargeting using z-stack seam carving, which allows light-field compression and extension while retaining angular consistency. Our algorithm first converts an input light field into a set of perspective-sheared focal stacks. It then applies 3D deconvolution to convert the focal stacks into z-stacks, and seam-carves the z-stack of the center perspective. The computed seams of the center perspective are sheared and applied to the z-stacks of all off-center perspectives. Finally, the carved z-stacks are converted back into the perspective images of the output light field. To our knowledge, this is the first approach to light-field retargeting. Unlike existing stereo-pair retargeting or 3D retargeting techniques, it does not require depth information.

    Birklbauer, C. and Bimber, O., Light-Field Retargeting, In proceedings of Eurographics (Computer Graphics Forum), 31(2), 295-303, 2012

    Light-Field Caching


    With the continuously increasing sensor resolutions of cameras, light field imaging is becoming a more and more practical extension to conventional digital photography. It complements postprocessing by synthetic aperture control, refocusing, as well as perspective and field-of-view changes. For being a true alternative to classical 2D imaging, however, the spatial resolution of light fields must be in the same megapixel-order as the resolution of today’s digital images. The additional angular resolution must also be adequately high to prevent sampling artifacts (in particular for synthetic re-focussing). This will quickly cause gigabytes rather than megabytes of data that have to be rendered with limited graphics memory. We describe a light-field caching framework that makes it possible to render very large light fields in real-time.

    Opelt, S. and Bimber, O., Light-Field Caching, ACM Siggraph (poster), 2011

    Light-Field Retargeting with Focal Stack Seam Carving


    With increasing sensor resolutions of digital cameras, light-field imaging is becoming more and more relevant, and might even replace classical 2D imaging in photography sooner or later. It enables, for instance, digital refocussing and perspective changes after capturing. Rescaling light fields to different resolutions and aspect rations, however, is challenging. As for regular image and video content, a linear scaling alters the aspect ratio of recorded objects in an unnatural way. In contrast, image and video retargeting utilizes a nonlinear and content-based scaling. Applying image retargeting to individual video frames independently does not retain temporal consistency. Similarly, applying image retargeting naively to the spatial domain of light fields will not retain angular consistency. We present a first approach to light-field retargeting. It allows compressing or stretching light-fields while retaining angular consistency.

    Birklbauer, C. and Bimber, O., Light-Field Retargeting with Focal Stack Seam Carving, ACM Siggraph (poster), 2011

 

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