Teaching

Generating Evacuation Tours through Deep Reinforcement Learning

[Type: Master Thesis, Focus: Computer Graphics, Machine Learning]

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Digital twins of indoor scenes are today more and more available in several forms such as 3D models, games and through building information modelling tools. In this project we want to leverage 3D building models for evacuation safety where evacuation tours are automatically generated for the scope of training people on evacuation paths in case ofemergency. The computed evacuation tours are used to guide and train people in case of emergency evacuation. Such tours could also be enhanced with inclusive features (e.g., an automated generation of tours that include a vocal description of what is seen, so that could be used to help people with visual impairments learning about evacuation paths). This project will focus on developing a methodology for automating the generation of trajectories of evacuation paths based on deep reinforcement learning. An agent is trained to learn how to automatically control a camera to generate an evacuation path that can be used for safety purposes.​

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​​Modeling and Rendering of Underwater Environments

[Type: Bachelor or Master Thesis, Focus: Computer Graphics]

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This project aims to create photorealistic underwater scenes that can be explored with a robotic agent. The core focus is on developing sophisticated computer graphics techniques to accurately simulate underwater optical properties like light refraction, scattering, and caustics, ensuring detailed and lifelike visuals. The project also involves programming a robotic agent to move through these scenes, interact with underwater terrain, and engage with marine flora and fauna, emulating real-life underwater exploration.

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​​Uncertainty Visualization for AI-based Segmentations of Medical CT Volumes

[Type: Bachelor or Master Thesis, Focus: Computer Vision, Medical]

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Various organs of the human patient can be segmented from CT scans and the corresponding greyscale axial views. AI models already achieve a high accuracy in the segmentation, but still need to be corrected. Instead of a binary classification mask as output, a heatmap may display the certainty of the segmentation and improve the understandability of the model.

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Effect of the Patient's Rotation Angle on Tissue Deformation in CT Volumes

[Type: Bachelor or Master Thesis, Focus: Computer Vision, Medical]

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While medical CT scans are generally taken in a supine position (lying on the back), during the actual surgery, patients are often positioned on their side. This leads to a deformation of internal organs so that their position does not match the CT scan anymore. The elasticity of various organ types may be estimated from tissue characteristics and used to predict absolute movement under the influence of gravity.

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Semantic Segmentation of Outdoor Scenes

[Type: Master Thesis, Focus: Computer Vision]

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This project aims to develop a deep learning model for semantic segmentation in outdoor environments, with a specific focus on identifying and classifying various types of vegetation such as trees, grass, shrubs, and other plant species. The student will work with datasets of outdoor images and leverage convolutional neural networks (CNNs) or other machine learning models to differentiate vegetation from non-vegetation areas. The outcome of the project will be a tool that can accurately map vegetation in outdoor scenes, potentially useful for applications like environmental monitoring, agriculture, and urban planning.

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​​Randomized Look-Up Data Structures for Spatial Data

[Type: Bachelor or Master Thesis, Focus: Computer Graphics]

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The goal of this project is to investigate how randomization can be used to engineer efficient data structures for queries on spatial data. Initially, a literature research should be conducted to compile an overview of what the state of the art is. Based on this, data structures that have only been proposed theoretically should be implemented or entirely new data structures should be developed and implemented. Experiments should then be conducted comparing the performance to that of conventional data structures (e. g. Kd-trees, octrees). Optionally, these newly developed data structures could make use of learned biases to make random decisions which might yield further performance improvements.​