Page Areas:

Additional Information:

Student Projects

Hier den Alternativtext zum Bild eingeben!

Displays Book (second edition)

Displays Book (second edition)

Displays Book (Korean edition)

Hier den Alternativtext zum Bild eingeben!

Displays Book (free ebook)

Hier den Alternativtext zum Bild eingeben!

Spatial Augmented Reality Book (free ebook)

Hier den Alternativtext zum Bild eingeben!

The Caleydo Project

Hier den Alternativtext zum Bild eingeben!

VIOSO smartprojecting

Hier den Alternativtext zum Bild eingeben!

Position Indication:


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.

Drone-Guided Light-Field Imaging

Topics: drone programming, light fields
Supervision: David Schedl, Indrajit Kurmi, Oliver Bimber
Contact: david.schedl(/\t)
Type: BSc Thesis, MSc Thesis (alone or team of two)
Parrot Bebop 2
Light fields have the potential to revolutionize digital imaging. By providing not only spatial (pixel) but also directional (light ray) information, they support synthetic refocusing, multi-perspective recording, depth-variant filtering, and much more. Commercial plenoptic cameras (e.g. Lytro) find applications in photography, cinema production, and virtual reality experience. In 2015, we presented a first approach to record light fields with conventional smartphones (see video) by guiding the user through the optimal sampling path while interactively recording light-field perspectives with the phone’s video camera.
In this project, we want a drone to be guided for autonomous light-field recording that has the potential to record light-fields of large objects, such as buildings. We use a Parrot Bebop2 and SDK for development and experiments.
This project is suited for BSc and MSc students interested in drone programming. It can be carried out alone or in a team.


Towards a Flexible, Scalable, and Transparent Thin-Film Camera

Topics: machine learning, optical sensors, image reconstruction, optics
Supervision: Oliver Bimber
Contact: oliver.bimber(/\t)
Type: MSc Thesis
meter reader
Since many years, we are working on the world’s first thin-film image sensor. It consists of a transparent and flexible plastic sheet which basically acts as an efficient 2D light guide. With advanced machine learning techniques, we are able to reconstruct images that are optically focussed on its surface. Thus, this sensor is similar in spirit to classical image sensors (such as CMOS or CDD chips) found in cameras. A camera, however, also requires an optical imaging system (e.g. a lens) for focussing images of objects on the sensor. In this LIT project, we are investigation thin-film imaging optics, such as coded apertures, collimators, diffusors, etc. that can be implemented as thin-film layers. We are also investigating modern machine learning approaches that are capable of reconstructing images with such layers. In this project, we start with software simulations - but the goal is to implement a physical sensor prototype. We are cooperating with Rice University and the Karlsruher Institute of Technology. It is suitable for MSc students interested in basic and interdisciplinary research, machine learning and optics.

Novel Computational Imaging Concepts

Topics: imaging, compressed sensing, image reconstruction, SLM Needs: Matlab, C++, CUDA
Supervision: Oliver Bimber, Bettina Heise
Contact: oliver.bimber(/\t), bettina.heise(/\t)
Type: BSc Thesis, MSc Thesis, MSc Practicum, BSc Practicum
Imaging and reconstructing objects by a single pixel camera concept became an emerging novel method introduced and demonstrated recently. This approach has the advantage that only a point detector is needed, which can be beneficial with respect to speed or the simple fact that area cameras are not available or to expensive (relevant e.g. for area camera sensitive in mid infrared or THz wavelength range or for non-linear optics applications). But also for the visible wave length range such a concept shows it challenges with respect to an effective reconstruction: There a sequence of recordings obtained as a (temporal) series of (spatial) projections of the object wave field modulated by an intensity or phase pattern are captured by the point detector. Meanwhile advanced methods are derived from this original technique applying e.g. multiple apertures or using the medium itself as tool for imaging. This leads to fully novel imaging methods and will enable further perspectives for imaging, which will provide insights where imaging as technique itself can be used: e.g. for material sciences, medical diagnostics, or in the depth of astronomy.
Here we have the possibility to implement and test in some principal optical settings such novel imaging concepts containing different types of spatial light modulator (SLM), such as a digital light processor (DLP) or a liquid crystal SLM-types.

In contact and supported by the imaging group at CDL MS-MACH/ZONA such configurations should be further develop, evaluated and optimized for different illumination schemes (coherent and incoherent), modulation types (spatial and temporal modulation) or material types, and in particular computationally effective reconstructions should be performed, where approaches of sparse signals and compressive sensing might be suitably included.

Image: [Phillips, 2016]

Visualization Meets Machine Learning

Topics: visualization, machine learning, D3.js, Javascript
Supervision: Martin Ennemoser, Marc Streit
Contact: martin.ennemoser(/\t)
Type: BSc Practicum, MSc Practicum, BSc Thesis, MSc Thesis
Machine Learning (ML) is currently one of the most studied fields in computer science and plays a major role in a wide range of applications, such as data mining, natural language processing, image recognition, and self driving cars. It is expected that the need for ML will further increase in the future. However, a major challenge is to get a better understanding of how ML algorithms, especially neuronal networks, function internally. The vast input data and large number of internal states makes it hard to analyze the internal behavior of these algorithms. Novel visualizations of ML algorithms need to be developed to open this black box. The goal of this project is to design and implement a visualization tool that allows users to analyze and evaluate the behaviour of neuronal (recurrent, LSTM) networks. This project will be carried out in collaboration with the JKU Institute of Bioinformatics. The solutions should be implemented using web-based technologies such as D3.js and JavaScript/TypeScript..

Novel Github Repository Visualization

Topics: visualization, source code management, D3.js, Javascript
Supervision: Holger Stitz, Marc Streit
Contact: holger.stitz(/\t)
Type: BSc Practicum, MSc Practicum, BSc Thesis, MSc Thesis
As the leading web platform for source code management and versioning control Github processes millions of commits every day. Especially for large repositories with many developers it is difficult for users to keep track of changes across different branches. The current Github user interface and state-of-the-art visualizations are limited in terms of scalability and not able to represent changes for different file types, such as source code, tabular data, and images, over time. The aim of this project is to design and implement an integrated representation for visualization changes of files associated with Github repositories. File changes must be quantified according to file type and considered over time. The visualization should allow users to get an overview over all changes and also drill-down to investigate changes of interest in more detail. The project should be implemented using web-based techniques such as D3.js.

Discovering Usage Patterns in Exploratory Data Analysis

Topics: visualization, big data, guidance, D3.js
Supervision: Holger Stitz, Marc Streit
Contact: holger.stitz(/\t)
Type: BSc Practicum, BSc Thesis, MSc Practicum, MSc Thesis
Exploratory data analysis allows domain experts to find new insights in large and complex data. Together with partners from Harvard University and University of Utah we developed visual data analysis tools for drug discovery, cancer genomics and other applications. In such exploratory tools we are able to track full history of the exploratory data analysis process including all loaded datasets, the visual and analytical tools applied to the data, and all interactions done by the user. Collecting this information results in a large and fast growing provenance graph. In order to make the information accessible to the user, an existing project at the institute deals with the interactive visualization of this graph and finding of similar states in the graph. The goal of this project is to run meta-analysis on the provenance graph to derive recurring chains of actions and usage patterns that can then be used to speed up the exploratory analysis and also to visually guide the user through the analysis by making suggestions.
The project will be implemented as part of the Caleydo project, which uses D3.js, JavaScript and Python as base technology.

Fusing Data Analysis with Data Wrangling

Topics: visualization, visual analytics, big data, D3.js, Python
Supervision: Marc Streit
Contact: marc.streit(/\t)
Type: BSc Practicum, BSc Thesis, MSc Practicum, MSc Thesis
Solving data intensive problems requires users to merge and analyze datasets that come from different sources, in various formats and semantics. The current workflow in visual data analysis is to first import and specify the semantics of the datasets in a standard importer dialog, before moving on to the actual visual analysis. However, when dealing with multiple, heterogeneous datasets the back and forth between data import and analysis results in a disruption of the workflow that hampers the sense making process. The goal of this project is to seamlessly fuse the data import with the data analysis. The project will be implemented in the Caleydo Web framework, which is based on D3.js, JavaScript and Python.