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Student Projects

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Displays Book (free image material)

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Spatial Augmented Reality Book (free ebook)

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VIOSO smartprojecting

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The Flagship Publication of the IEEE Computer Society

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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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Theses and Practical Courses

The Institute of Computer Graphics continuously supervises students on all levels (BSc, MSc as well as PhD) in computer graphics, computer vision, visual computing, visualization, or related fields. Each student will be individually advised.... continue reading.
Interested and motivated students are invited to contact us at any time during the semester.

Selected Student Work

A selection of the finest student projects can be found here.

Open Theses Topics

The following list of topics is currently available and can be chosen at any time in the semester. The topics are assigned to our main research areas: Light-Field Processing, Transparent and Flexible Image Sensor and Visual Analytics. Topics will be assigned on a first-come, first-served basis. We also welcome own suggestions that are related to visual computing. LaTex templates for theses and practical course reports are available in German and English. If you have general questions regarding doing a thesis at our institute, feel free to contact us.

Light-Field Processing

Light Fields

Light-field photography extends conventional digital photography by multi-perspective information that enables 3D processing and viewing (including synthetic refocusing, 3D depth reconstruction, large depth-of-field photography, auto-stereoscopic viewing, etc.). 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). Our group develops novel light-field processing algorithms.

Supervision: Clemens Birklbauer, David Schedl, Oliver Bimber
Contact: cg(/\t)

Light-Field Rendering for Virtual Reality Glasses

Type: BSc Practicum, MSc Practicum

Oculus Rift

Virtual reality glasses, such as the Oculus Rift, are becoming popular, affordable and practical, due to compact hardware. These devices display stereoscopic content with a wider field of view than regular displays, supporting an immersive experience for the user. Additionally the viewer's head position, orientation and movement are tracked and the display can be altered accordingly. However, available content for such devices is mostly limited to games or live-renderings. Precalculated stereoscopic pictures and videos do not support altering the viewer's head orientation, due to the horizontal camera displacement (i.e. fails if the user tilts his head sideways). 4D light fields store additional data that is needed for orientation-aware stereoscopic displaying.
The goal of this project is to incorporate orientation-aware stereoscopic light-field rendering into our software, while solving challenges such as interpolation, rendering and navigation. Students will have the opportunity to work with a development version of the "Oculus Rift" virtual reality glasses, and will get insights on light-field processing.

Generalized Depth-of-Field Light-Field Rendering

Type: BSc Practicum, BSc Thesis, MSc Practicum

Generalized Depth-of-Field

In photography and cinematography shallow depth of field is used to emphasize a subject in a scene, while de-emphasizing the foreground and background. With light fields it is possible to shift the focus while rendering, thus alternating the sharpness and blurriness of objects after recording. While photo-realistic rendering only supports a single focus plane, thus bringing objects in a certain depth range into focus, it might be desirable to focus or defocus multiple objects at different depths. With non-photo-realistic light field rendering this is achievable.
This project's goal is to develop an application for non-photo-realistic light field rendering. A method for robust user-guided marking of certain objects (segmentation) in a light field has to be implemented. The light field will be rendered based on these user markups, allowing the creation of novel (focus) effects only achievable with light fields.
For skilled students (e.g. BSc thesis), this project can be extended to light-field video processing.

Gradient Domain Light-Field Processing

Type: BSc Practicum, MSc Practicum

Gradient domain processing for images is used for example to seemlessly merge images, stitch panoramas, enhance images or create artistic effects. Examples for this can be found here: GradientShop
In a light field we do not only have gradient information in each perspective image but get as well gradients over different views. In this project we want to explore the advantages of these 4D gradient information e.g. for stitching two light fields or to develop interesting new filters e.g. for non-photorealistic rendering. A solution for converting 4D gradient information back to light fields and various filters or algorithms applicable to the gradients have to be implemented and explored during this project.

Transparent and Flexible Image Sensor

Image Sensor

Our institute has developed the world's first fully transparent, flexible scalable image sensor, called LumiConSense. The sensor consists of a thin-film luminescent concentrator. This special transparent foil transports light that falls on its surface towards its edges, where line scan cameras measure the amount of transported light. From the measured data we reconstruct an image that was focused on the surface of the luminescent concentrator foil. This can either be done by solving a system of linear equations or by using tomographic image reconstruction techniques that are known from CT scanners.

Supervision: Alexander Koppelhuber, Oliver Bimber
Contact: cg(/\t)

Non Uniform Sampling

Type: BSc Practicum, BSc Thesis, MSc Practicum, MSc Thesis

The goal of this project is to improve the image quality of the reconstructed images by evaluating the possibilities of non-uniform sampling for the calibration of the light transport matrix. Currently this matrix is determined by sequentially projecting a single pixel onto the LC image sensor. After this calibration procedure the light transport matrix contains the information on how much each pixel of an entire image contributes to the measured signals of the line scan cameras. Instead of using square pixels at a uniform sampling grid, it is also possible to sample random grid positions with non-square pixels of different sizes. The question is: What are the optimal sizes, forms and sampling positions of such pixels to optimize the quality of reconstructed images.

Low Level Sensor Classification

Type: BSc Practicum, BSc Thesis, MSc Practicum, MSc Thesis

The goal of this project is to investigate the possibility of directly using the raw signals of the line scan cameras to extract useful information without reconstructing a 2D image and without the calibration of the light transport matrix. For example, is it possible to detect rough object shape or motion gestures only from the 1D signal of the line scan cameras? The aim is to find a robust method to solve these kind of problems by the means of machine learning and classification methods.

Visual Analytics


Visual Analytics is an emerging and fast developing field that combines the strengths of graphical visualization with the power of analytical reasoning. It supports discovering new and unknown insights by finding relations, patterns, trends, or outliers in potentially large and complex data. As the unique sense-making skills of human analysts are tightly coupled with interactive visualization techniques, Visual Analytics can lead to discoveries that neither a computer nor a human can make alone. Pairing both in an efficient way is the key to future analysis.

Supervision: Marc Streit, Samuel Gratzl, Holger Stitz
Contact: cg(/\t)

Degree-of-Interest Adapted Edge-Bundling

Type: BSc Thesis, MSc Thesis, BSc Practicum, MSc Practicum

Making sense of graphs with thousands or even millions of nodes and edges is a common data analysis problem. Visualizing these large-scale graphs beyond just creating a “giant hairball” is challenging. Edge-bundling is a well known technique that can significantly reduce the visual clutter in the visualization (see Figure). By grouping edges to bundles, major trends become visible – at the cost that details about the single edges get lost. The idea of this thesis is to lower the tension for edges that are of current interest, while still tightly bundling all the other edges of the graph. What edges are relevant can be either defined interactively by the user or programmatically by detecting highlights in the data, such as anomalies in a network traffic stream.
The goal of this project is to design and implement an edge-bundling algorithm that is able to incorporate the above described degree-of-interest information. The technique should be implemented as a web-based prototype using the popular D3 framework.

Visual Data Analysis Across Web Applications

Type: BSc Practicum, BSc Thesis, MSc Practicum, MSc Thesis

Over the last few decades many fields of science have been confronted with tremendous amounts of data and continuously increasing annual growth rates. The sheer amount of data, however, is only one aspect of the problem. In order to solve complex analysis questions, it is necessary to cope with heterogeneous data sets from different sources, on distinct levels of scale and stored in various formats and types such as text, maps, graphs and images. Tailored visual analysis tools, such as the one demonstrated in the video, are well suited for solving a specific, predefined analysis task. However, it is highly unrealistic that it will be possible to create a super-application that covers all kinds of data and analysis tasks from different domains. Thus, users want to use a combination of existing applications to perform their analysis. However, the single tools are usually not integrated with each other.
The goal of this thesis is to develop a meta-visualization that is able to guide the analyst in such a multi-application scenario. In order to realize a visual integration of the single web-based tools, it will be essential to find ways for navigating, arranging, extracting, and adapting the individual application's interfaces.

LineUp Web Port

Type: BSc Practicum, MSc Practicum

Rankings are omnipresent. They have the important function of helping us to navigate content and provide guidance as to what is considered “good”, “popular”, “high quality”, and so on. In recent work, we have developed LineUp (, a novel visualization technique for creating, analyzing, and comparing multi-attribute rankings. LineUp allows us, for example, to prioritize tasks or to evaluate the performance of universities relative to each other. The technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. The current LineUp prototype is implemented in Java using OpenGL and is part of the Caleydo project. The goal of this project is to port the LineUp visualization technique to the Web using HTML5, JavaScript, and SVG. The web port should provide the same features as the original implementation with additional support for loading, sharing, and saving of ranking results.

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