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Table of available RSinCS/BSc thesis projects
| Supervisor | Abbrv. |
|---|---|
| Prof. Dr. Jiri Kosinka | JK |
| Dr. Steffen Frey | SF |
| Dr. Christian Kehl | CK |
This page provides a list of current BSc/MSc projects offered by the SVCG group and their status. The column called Sup. indicates the main supervisor of the project (see the table on the right), and the column labelled # indicates how many positions are currently available in a particular project.
| Type | Sup. | # | Project |
|---|---|---|---|
| BSc | JK | 1 | Precision of drawing in VR |
| What is the precision of drawing simple shapes, such as line segments and circles, in virtual reality? How do various guide tools help? You will investigate these research questions in this project. You should own a computer able to drive a VR headset. If you do not own a VR headset, you can borrow one from us or the CIT. You can build on recent Tilt Brush projects: see here and here, and the references cited therein, or, even better, work in OpenBrush. | |||
| 1 | Precision of shape matching in VR | ||
| What is the precision of matching simple shapes and scenes, such as sphere, cubes and their combinations, in virtual reality? How do various support tools help? You will investigate these research questions in this project. You should own a computer able to drive a VR headset. If you do not own a VR headset, you can borrow one from us or the CIT. You can build on recent Tilt Brush projects: see here and here, and the references cited therein, or, even better, work in OpenBrush. | |||
| 0 | Point cloud processing and visualization with Enatom | ||
| This project will be done in collaboration with Enatom, a company based in Groningen. They provide a virtual dissection room with photorealistic content in which knowledge transfer and testing can take place in an intuitive way at any place and any time. In this project, you will explore means of processing point clouds of scanned bodies so that they can be efficiently visualized (by exploring options such as surface reconstruction or Gauss splatting). | |||
| 0 | Visual Context and Activity Logging for Cry Detection in the NICU | ||
| This is a project in collaboration with Neolook; see this PDF. | |||
| 0 | Nanocar serious game | ||
| In collaboration with Ben Feringa's team, this tool/game has been developed. We'd like to take this further, by adding a better story to it, making it more educational, running user studies, etc. If you are familiar with Unity, this might be the right project for you. | |||
| BSc/MSc | SF | 1 | Neural Upscaling for Scientific Visualization: Benchmarking and Fine-Tuning |
| This project investigates whether deep-learning-based super-resolution can be safely applied to scientific visualization tasks such as volume and flow rendering. Commonly used super-resolution methods are typically trained on movies or video games and may introduce hallucinated features or suppress critical fine-scale structures in scientific data. The project focuses on classifying the errors that arise when using off-the-shelf methods and addressing these issues through fine-tuning on scientific visualization images.
The workflow involves building an open-source neural super-resolution pipeline and generating paired low- and high-resolution scientific renderings, optionally including depth or motion information. Models are first applied without additional training, then fine-tuned on domain-specific data using losses that emphasize edge preservation, intensity fidelity, and structural accuracy. A comparison is also made with a proprietary hardware-accelerated super-resolution method by upscaling the same scientific scenes using its available API. Quality is assessed using both general image metrics and domain-specific measures, including preservation of small features, consistency across frames, and sensitivity of derived analyses. The project evaluates how the different approaches respond to characteristic scientific patterns such as thin isosurfaces, vortices, or sharp scalar gradients, identifying when neural super-resolution provides reliable acceleration for scientific visualization and where standard high-resolution rendering remains necessary. | |||
| BSc/MSc | SF | 0 | Pattern-Aware Transfer Functions for Scientific Volume Rendering |
| Volume rendering is a powerful tool for visualizing complex 3D datasets, but traditional transfer functions typically map color and opacity based only on scalar values at individual points, sometimes including derivatives. This approach can miss subtle structures or repeating patterns that span multiple voxels. This project explores transfer functions that operate on local spatial regions ("blocks") rather than single points, enabling visualizations to highlight patterns, textures, and recurring structures within volumetric data. Key challenges include efficiently representing these spatial patterns, extracting them from the data, and designing intuitive ways for users to explore and manipulate them.
The project focuses on scientific visualization and deep learning. It investigates methods for block-level feature extraction—such as deep learning-based descriptors, 3D convolutional features, or classical approaches like local histograms or PCA—and applies clustering or pattern classification to identify recurring structures. Interactive tools can then assign color and opacity to clustered patterns, creating more expressive and informative visualizations. The project involves working with scientific datasets, experimenting with both data-driven and user-driven approaches, and contributing to the development of more intuitive techniques for understanding complex volumetric data. | |||
| BSc/MSc | SF | 1 | Data-Driven Glyph Design for Ensemble Visualization |
| Grids are a common way to visualize large data collections, but they involve a trade-off: higher resolution allows more thumbnails to be displayed, yet each glyph has only a small space to convey meaningful information. Efficient glyph design is therefore crucial to highlight similarities and differences across many data instances.
This project focuses on creating expressive glyphs for visualizing structures and processes in porous media through a two-step approach. First, a glyph design space is defined using a small set of simple primitives that can be varied in size, position, and color. These variations support rapid, pre-attentive comparison of patterns. Second, glyphs are parametrized in a data-driven way using a Siamese network architecture to capture feature similarities between data samples. This representation allows subtle commonalities and differences to be clearly reflected, making large ensemble comparisons more intuitive and informative. | |||
| BSc/MSc | SF | 1 | Enhancing Sca2Gri: Efficient and Holistic Scatterplot Visualization |
| Sca2Gri is a scalable post-processing method for large-scale scatterplots that reduces visual clutter by gridifying glyph representations. It is designed for data analysis scenarios involving millions of data points, far beyond what traditional scatterplot rendering techniques can handle effectively.
This project explores ways to improve both the performance and the expressiveness of Sca2Gri. One focus is optimizing the selection of data points for rendering grid glyphs using specialized data structures. Range trees combined with fractional cascading present a promising approach. Range trees efficiently handle range queries in n-dimensional data, and fractional cascading can reduce the computational complexity to that of a one-dimensional range tree, potentially speeding up queries for visualization. Another key aspect is the aggregation of data points within each glyph to provide a more holistic view. Rather than showing a single representative point, each glyph could summarize the full range of underlying data—through averaging or other aggregation techniques—while maintaining interactive exploration capabilities, such as a draggable lens. This project seeks to address questions such as:
By combining efficient data structures with aggregation and interaction techniques, this project aims to make scatterplot visualization both faster and more informative, supporting deeper insights into complex datasets. | |||
| BSc/MSc | SF | 1 | PARViT: Perioperative Augmented Reality Visualization via Visual Information Transformers |
| PARViT is envisioned as an ML-based perioperative support system that uses augmented reality to guide surgeons through the critical preparation phase of rectal cancer surgery. This phase requires careful dissection of four structural “pillars,” each composed of five partial steps, to enable a radical resection and favorable oncological outcomes.
The project focuses on learning from expert ratings of dissection progress in a large video database. These ratings form the basis for automated feedback and for studying how varying levels of preparation correlate with three-year oncological outcomes. Technically, the project aims to build PARViT on top of a Vision Transformer (ViT) architecture. Surgery frames are split into patches and processed through transformer blocks to produce frame embeddings that capture visual features relevant for assessing progress. The model will be pre-trained on large image datasets (e.g., ImageNet-21k, Medical ImageNet) and fine-tuned on labeled surgery videos. Self-attention and multi-headed attention mechanisms will enable the system to identify important structures and contextual relationships in each frame. Overall, the project aims to explore how such a system can support consistent, safe tissue preparation and reduce perioperative complications. It will be conducted in collaboration with the UMCG. Objectives:
Detect the start of the preparation phase, track progress within each pillar on a 0–5 scale, and identify the transition to tumor removal. Maintain an internal representation of pillar-specific progress based on frame-level classifications learned from expert-annotated sequences.
Use three-year outcome data to examine whether incomplete preparation (e.g., reaching only 3/5 in a pillar) is associated with increased recurrence risk, thereby validating the clinical relevance of the preparation states. Stretch Goals:
Retrieve similar, well-executed reference segments from the database by comparing internal embeddings with those from the ongoing procedure.
Highlight influential image regions using attention maps to support interpretation and identify structures relevant for the assessed state.
Prototype AR overlays that present state information, best-practice examples, and attention highlights in a clear, unobtrusive manner, with long-term potential for integration into robotic platforms such as the Da Vinci system. | |||
| BSc/MSc | SF / JK | 2 | Extending Hieradex, a hierarchical multi-volume visualization method [2 projects] |
| The Hierarchical dataset explorer (Hieradex) is a visualization method for large-scale image and volumetric datasets. This method leverages the hierarchical grid structure of the Level-of-Detail Grid (LDG)[1] to visualize ensembles across multiple granularities, enabling exploration of each dataset sample itself as well as the data distribution and relations within a dataset. The implementation of Hieradex is publicly available here and a demo of its functionality can be seen here. You can find a set of project topics pertaining to the improvement of Hieradex as a dataset exploration tool below. Any questions related to these projects may be redirected to d.h.boerema@rug.nl.
[1] Frey, S. (2022), Optimizing Grid Layouts for Level-of-Detail Exploration of Large Data Collections. Computer Graphics Forum, 41: 247-258. [https:/doi.org10.1111cgf.14537](https:doi.org10.1111/cgf.14537) * Project 1 -- Data-adaptive transfer function estimation One of the key features of Hieradex is the ability to design and control the transfer function through the UI. However, transfer function design is a complex process in which many visualization principles such as clarity, focus and accessibility need to be balanced. To help the user get an initial estimate for a transfer function, a rudimentary 'transfer function exploration space' is implemented in which a simple set of transfer functions is provided as a starting point for further design.Although this method helps in providing the user with an initial guess, further tuning is always needed due to the simplicity of the provided functions. This project aims to explore the possibilities of transfer function design in this context with the end goal of providing the user with informative transfer functions which require significantly less tuning. This could be achieved through methods such as:
By providing a better set of transfer functions to the user, the dataset exploration process becomes both more efficient by cutting down time needed for tuning and more insightful by enabling more informative visualizations. * Project 2 -- Exploration of large-scale mesh datasets To further expand the capabilities of Hieradex as a visualization tool, a mesh renderer could be integrated to support the visualization and exploration of mesh databases. This would allow Hieradex to visualize a broad range of different types of data collections (images, volumes, meshes). Many of the tools used during volume rendering, such as level-of-detail rendering, can be repurposed to serve meshes as well. Aside from developing and integrating a mesh renderer, the main challenge in this project is to find ways to enhance the dataset exploration process specifically for meshes. For volume rendering, tools such as a user-configurable transfer function are implemented to aid in both the visualization and exploration of the data samples themselves. By developing similar tools specifically for meshes, it becomes easier to sift through and distinguish individual samples in otherwise largely homogenous mesh datasets. | |||
| BSc | CK | 0 | available project spots. Topics: see below. |
| CK | 0 | Oceanic transport and accurate coastal deposition of marine plastics | |
| One of the primary challenges in particle-based ocean simulations is the treatment of coastal boundaries. Due to flow-field resolution constraints, it is difficult to model coastal dynamics, which is important for studying the ecosystem impact. Advecting particles exclusively by fluid velocity gets them stuck on the shoreline, which is physically incorrect. Existing studies propose alternative solutions to address this issue, as well as early code demonstrations of other simulation engines. The objective of this thesis is to investigate alternative approaches to the handling of Lagrangian particles in coastal areas, and extend the existing C++ real-time simulation in that way.
Feel free to contact Christian by email for detailed project descriptions. A short description is available at here. | |||
| 1 | Tidally-correct flow transport of plastic litter in the North Sea | ||
| Tidal effects have a substantial impact on deposition of plastics in coastal areas. Tides are a major flow velocities at ocean shorelines, and they dynamically raise- and drop the water level, resulting in early deposition and delayed return of transported material to the ocean. The objective of this thesis is to integrate tidal velocities in the existing Lagrangian oceanic transport simulation, and to adapt the simulation to dynamically-changing sea surface heights.
Despite the available material, the project requires advanced proficiency in computational fluid dynamics (CFD), linear algebra and numerical computing, with a proven track-record in this domain. It is ideal for students following a double-degree with either BSc Mathematics or BSc Applied Physics. Feel free to contact Christian by email for detailed project descriptions. A short description is available at here. | |||
| CK & SF | 0 | Image analysis of polarized rock thin-sections | |
| The ongoing gas depletion of the Groningen natural gas field leads to bedrock subsidence and associated seismic activity. Estimating the probability of local seismic activity requires a reliable rock model of Groningen’s gas field. In this interdisciplinary project with the Geo-Energy group at the university’s Energy and Sustainability research institute, we together facilitate the rock model with properties derived from microscope scans of the rock’s mineral structure. Those microscope scans – so-called ‘thin-sections’ – are spectral image stacks, which need to be (a) co-registered, (b) segmented, (c) labelled and (d) numerically described. The user-guided outcome of the project then facilitates the increasingly-rapid creation of training references for automated deep-learning procedures in later projects. Thus, the interactive tools developed in this project bridge the large gap in appropriate training data to even make deep-learning a viable future vision.
Feel free to contact Christian by email or Steffen by email for detailed project descriptions. Description teasers for the segmentation is available. | |||
| CK | 0 | Fast Stochastic Simulation of Porous 3D Structures | |
| Geological modelling is a key procedure in modern energy systems, be it traditional fossil fuels (e.g. oil and natural gas), or sustainable- and environmentally-cleaner carbon capture-and-storage (CCS), geothermal energy, or long-term nuclear-waste disposal. Recent student projects explored the potential of interactive graphical models for geology education on stochastic geo-modelling.
A major inhibitor in pushing this concept further is the availability of geologically-plausible 3D training images of porous media. In order to close the substantial data-inventory gap, fast stochastic 3D volume simulators for those shapes are required. The research objective of this (team) project is to develop those 3D porous-media simulators using geometrical- or texture-driven approaches. Feel free to contact Christian by email for detailed project descriptions. The description teaser here . | |||
| 0 | The Life of a Plastic Particle - Visual Storytelling in Stereoscopic-VEs | ||
| Particle-based Lagrangian ocean simulation are common for estimating the transport of marine plastic. Micro- or nanoplastic particles are human-health threats: they are consumed by fish, and thus later by humans. The small plastic particulates enter the blood-stream, causing cardio-circulatory problems. Communicating the transport of microplastics to the general public is challenging: the particles are so minuscule that their transport is difficult to accurately visualize. A new approach in this public communication is personalized storytelling, where the presentation zooms in on an individual particle and its life-cycle.
The objective of this research-driven BSc project is the design of a visual storyline for telling the life-cycle of nanoplastic particles. This visual story is then to be implemented in a fully 3D visual animation that runs on the theatric stereoscopic VE of CIT. Feel free to contact Christian by email for detailed project descriptions. The description teaser is available here . |