Node and Edge Selection in an Immersive Environment
In this project you will develop an immersive Unity application for visualizing graph data. Then, multiple techniques for selecting nodes/edges should be developed. The essential goal is to support easy-to-use selection with low error rate, even in cluttered and occluded areas. To compare the techniques, a user study should be conducted.
Selection is one of the most important interaction techniques required for successful visual analysis. This is also the case for network analysis in immersive AR or VR environments. While there are standard ways that often work well, like using a virtual hand ray to select objects, 3D network data often requires more sophisticated methods, as the level of clutter and occlusion can be high. Furthermore, network-specific semantics and multi-modal approaches (e.g. combinations with eye tracking) can be used to make selections faster and more succesful. With this project, we want to develop and compare multiple, new selection approaches for networks in 3D.
- Work alone or as team of two students.
- Get familiar with Unity and the technical requirements for developing a VR/AR application for graph exploration
- Create a framework for visualizing graph data and develop different techniques for selection network-specific objects
- Create synthetic graph data with different properties to test your implementation
- Evaluation of the developed techniques by means of a user study.
- Basic knowledge about graphs and visual analytics
- Advanced programming skills in Java or C#
- Good conceptual skills (software architectures)
- Useful: Git, Unity, VR-Experience, experience with user studies
- Scope: Bachelor/Master
- Yu, Difeng, et al. "Fully-occluded target selection in virtual reality." IEEE transactions on visualization and computer graphics 26.12 (2020): 3402-3413.
- Delamare, William, Maxime Daniel, and Khalad Hasan. "MultiFingerBubble: A 3D Bubble Cursor Variation for Dense Environments." CHI Conference on Human Factors in Computing Systems Extended Abstracts. 2022.