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Integrative Computational Design and Construction for Architecture (T3.0)
Research
Research Projects
RP 28-1 – Visual Support for Architectural Design and Construction

Visual Support for Architectural Design and Construction

Research Project 28-1 (RP 28-1)

Weiße Schrift

VISUAL SUPPORT FOR ARCHITECTURAL DESIGN AND CONSTRUCTION

This project will develop methods to support and evaluate design and construction processes in the architectural context. We will achieve this by incorporating eye tracking methodology combined with visualization and human-computer interaction techniques. This combination will help investigate such processes from a perceptual and cognitive perspective, providing insights into how people work with new visual interfaces and how they perceive virtual and real-world architecture. New techniques will be developed to handle spatio-temporal data in such a context with post-experimental evaluation as well as live processing with gaze-based interaction techniques. Specifically, we will focus on the analysis and support of collaborative procedures (e.g., gigamapping) and design parameter space exploration. Both scenarios provide new challenges for analysis in social and desktop-based situations. We will then extend our research on scenarios in spatial contexts, i.e., movement through real, virtual, and augmented buildings. Our research will finally be extended by scenarios in fabrication and construction environments where in addition to evaluation procedures, the development of techniques for gaze-based human-machine interaction promises new ways to support working routines.

PRINCIPAL INVESTIGATOR

Dr. Kuno Kurzhals
Visualization Research Center (VISUS), University of Stuttgart

TEAM

Nelusa Pathmanathan (VISUS)

PEER-REVIEWED PUBLICATIONS

2023
1. Kurzhals, K. (2023). Privacy in Eye Tracking Research with Stable Diffusion. Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, 1–7. https://doi.org/10.1145/3588015.3589842
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  @inproceedings{Kurzhals2023, abstract = {Image-generative models take textual prompts as input and generate almost arbitrary image content based on the underlying training data. This technology is rapidly developing and produces better results with each new generation of trained models. Apart from the application to create artwork, we see potential in deploying such models for eye-tracking research with respect to anonymizing content in visual stimuli. One feature of such models is the ability to take an image as input and adjust content according to a prompt. Hence, privacy-preserving visualization of stimuli can be achieved for static images and videos by slightly adjusting content to anonymize persons, text, and other sensible sources. In this work, we will discuss how this process can be applied to the presentation and dissemination of results with respect to privacy issues resulting from eye-tracking experiments.}, address = {New York, NY, USA}, author = {Kurzhals, Kuno}, booktitle = {Proceedings of the 2023 Symposium on Eye Tracking Research and Applications}, day = 30, doi = {10.1145/3588015.3589842}, isbn = {9798400701504}, location = {Tubingen, Germany}, month = {05}, pages = {1–7}, publisher = {Association for Computing Machinery}, series = {ETRA '23}, title = {Privacy in Eye Tracking Research with Stable Diffusion}, url = {https://doi.org/10.1145/3588015.3589842}, year = 2023 }
2. Pathmanathan, N., Öney, S., Becher, M., Sedlmair, M., Weiskopf, D., & Kurzhals, K. (2023). Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments. Computer Graphics Forum, 42(3), Article 3. https://doi.org/10.1111/cgf.14838
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  @article{pathmanathan2023been, abstract = {Abstract The distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real-world environments. Our method is based on a space-time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think-aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.}, author = {Pathmanathan, N. and Öney, S. and Becher, M. and Sedlmair, M. and Weiskopf, D. and Kurzhals, K.}, doi = {https://doi.org/10.1111/cgf.14838}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14838}, journal = {Computer Graphics Forum}, language = {eng}, number = 3, pages = {385-396}, title = {Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14838}, volume = 42, year = 2023 }
3. Öney, S., Pathmanathan, N., Becher, M., Sedlmair, M., Weiskopf, D., & Kurzhals, K. (2023). Visual Gaze Labeling for Augmented Reality Studies. Computer Graphics Forum, 42(3), Article 3. https://doi.org/10.1111/cgf.14837
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  @article{oney2023visual, abstract = {Augmented Reality (AR) provides new ways for situated visualization and human-computer interaction in physical environments. Current evaluation procedures for AR applications rely primarily on questionnaires and interviews, providing qualitative means to assess usability and task solution strategies. Eye tracking extends these existing evaluation methodologies by providing indicators for visual attention to virtual and real elements in the environment. However, the analysis of viewing behavior, especially the comparison of multiple participants, is difficult to achieve in AR. Specifically, the definition of areas of interest (AOIs), which is often a prerequisite for such analysis, is cumbersome and tedious with existing approaches. To address this issue, we present a new visualization approach to define AOIs, label fixations, and investigate the resulting annotated scanpaths. Our approach utilizes automatic annotation of gaze on virtual objects and an image-based approach that also considers spatial context for the manual annotation of objects in the real world. Our results show, that with our approach, eye tracking data from AR scenes can be annotated and analyzed flexibly with respect to data aspects and annotation strategies.}, author = {Öney, S. and Pathmanathan, N. and Becher, M. and Sedlmair, M. and Weiskopf, D. and Kurzhals, K.}, doi = {10.1111/cgf.14837}, journal = {Computer Graphics Forum}, language = {eng}, month = {06}, number = 3, pages = {373--384}, publisher = {Wiley}, title = {Visual Gaze Labeling for Augmented Reality Studies}, url = {/brokenurl# https://doi.org/10.1111/cgf.14837}, volume = 42, year = 2023 }
2022
1. Abdelaal, M., Schiele, N. D., Angerbauer, K., Kurzhals, K., Sedlmair, M., & Weiskopf, D. (2022). Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations. IEEE Transactions on Visualization and Computer Graphics, 1–11. https://doi.org/10.1109/TVCG.2022.3209427
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  @article{9908291, abstract = {This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (n = 150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.}, author = {Abdelaal, Moataz and Schiele, Nathan D. and Angerbauer, Katrin and Kurzhals, Kuno and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.1109/TVCG.2022.3209427}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-11}, title = {Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations}, year = 2022 }
2. Koch, M., Kurzhals, K., Burch, M., & Weiskopf, D. (2022). Visualization Psychology for Eye Tracking Evaluation. arXiv. https://doi.org/10.48550/ARXIV.2204.12860
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  @misc{https://doi.org/10.48550/arxiv.2204.12860, author = {Koch, Maurice and Kurzhals, Kuno and Burch, Michael and Weiskopf, Daniel}, copyright = {arXiv.org perpetual, non-exclusive license}, doi = {10.48550/ARXIV.2204.12860}, publisher = {arXiv}, title = {Visualization Psychology for Eye Tracking Evaluation}, url = {https://arxiv.org/abs/2204.12860}, year = 2022 }
3. Koch, M., Weiskopf, D., & Kurzhals, K. (2022). A Spiral into the Mind: Gaze Spiral Visualization for Mobile Eye Tracking. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 5(2), Article 2. https://doi.org/10.1145/3530795
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  @article{10.1145/3530795, abstract = {Comparing mobile eye tracking data from multiple participants without information about areas of interest (AOIs) is challenging because of individual timing and coordinate systems. We present a technique, the gaze spiral, that visualizes individual recordings based on image content of the stimulus. The spiral layout of the slitscan visualization is used to create a compact representation of scanpaths. The visualization provides an overview of multiple recordings even for long time spans and helps identify and annotate recurring patterns within recordings. The gaze spirals can also serve as glyphs that can be projected to 2D space based on established scanpath metrics in order to interpret the metrics and identify groups of similar viewing behavior. We present examples based on two egocentric datasets to demonstrate the effectiveness of our approach for annotation and comparison tasks. Our examples show that the technique has the potential to let users compare even long-term recordings of pervasive scenarios without manual annotation.}, address = {New York, NY, USA}, articleno = {20}, author = {Koch, Maurice and Weiskopf, Daniel and Kurzhals, Kuno}, doi = {10.1145/3530795}, issue_date = {May 2022}, journal = {Proceedings of the ACM on Computer Graphics and Interactive Techniques}, month = {05}, number = 2, numpages = {16}, publisher = {Association for Computing Machinery}, title = {A Spiral into the Mind: Gaze Spiral Visualization for Mobile Eye Tracking}, url = {https://doi.org/10.1145/3530795}, volume = 5, year = 2022 }
4. Kurzhals, K., Becher, M., Pathmanathan, N., & Reina, G. (2022). Evaluating Situated Visualization in AR with Eye Tracking. 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), 77–84. https://doi.org/10.1109/BELIV57783.2022.00013
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  @inproceedings{9978467, abstract = {Augmented reality (AR) technology provides means for embedding visualization in a real-world context. Such techniques allow situated analyses of live data in their spatial domain. However, as existing techniques have to be adapted for this context and new approaches will be developed, the evaluation thereof poses new challenges for researchers. Apart from established performance measures, eye tracking has proven to be a valuable means to assess visualizations qualitatively and quantitatively. We discuss the challenges and opportunities of eye tracking for the evaluation of situated visualizations. We envision that an extension of gaze-based evaluation methodology into this field will provide new insights on how people perceive and interact with visualizations in augmented reality.}, author = {Kurzhals, Kuno and Becher, Michael and Pathmanathan, Nelusa and Reina, Guido}, booktitle = {2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)}, doi = {10.1109/BELIV57783.2022.00013}, month = {10}, pages = {77-84}, title = {Evaluating Situated Visualization in AR with Eye Tracking}, year = 2022 }
2021
1. Franke, M., Martin, H., Koch, S., & Kurzhals, K. (2021). Visual Analysis of Spatio-temporal Phenomena with 1D Projections. Computer Graphics Forum, 40(3), Article 3. https://doi.org/10.1111/cgf.14311
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  @article{Franke_2021, abstract = {It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.}, author = {Franke, M. and Martin, H. and Koch, S. and Kurzhals, K.}, doi = {https://doi.org/10.1111/cgf.14311}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14311}, journal = {Computer Graphics Forum}, number = 3, pages = {335--347}, title = {Visual Analysis of Spatio-temporal Phenomena with 1D Projections}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14311}, volume = 40, year = 2021 }
2. Kurzhals, K. (2021). Image-Based Projection Labeling for Mobile Eye Tracking. ACM Symposium on Eye Tracking Research and Applications, 1–12. https://doi.org/10.1145/3448017.3457382
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  @inproceedings{Kurzhals2021, abstract = {The annotation of gaze data concerning investigated areas of interest (AOIs) poses a time-consuming step in the analysis procedure of eye tracking experiments. For data from mobile eye tracking glasses, the annotation effort is further increased because each recording has to be investigated individually. Automated approaches based on supervised machine learning require pre-trained categories which are hard to obtain without human interpretation, i.e., labeling ground truth data. We present an interactive visualization approach that supports efficient annotation of gaze data based on image content participants with eye tracking glasses focused on. Recordings can be segmented individually to reduce the annotation effort. Thumbnails represent segments visually and are projected on a 2D plane for a fast comparison of AOIs. Annotated scanpaths can then be interpreted directly with the timeline visualization. We showcase our approach with three different scenarios.}, address = {New York, NY, USA}, author = {Kurzhals, Kuno}, booktitle = {ACM Symposium on Eye Tracking Research and Applications}, day = 25, doi = {10.1145/3448017.3457382}, isbn = {9781450383448}, location = {Virtual Event, Germany}, month = {05}, pages = {1–12}, publisher = {Association for Computing Machinery}, series = {ETRA '21 Full Papers}, title = {Image-Based Projection Labeling for Mobile Eye Tracking}, url = {https://doi.org/10.1145/3448017.3457382}, year = 2021 }

OTHER PUBLICATIONS

DATA SETS

2023
1. Pathmanathan, N., Öney, S., Becher, M., Sedlmair, M., Weiskopf, D., & Kurzhals, K. (2023). Replication Data for: Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments. DaRUS. https://doi.org/10.18419/darus-3383
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  @dataset{pathmanathan2023replication, abstract = {The file contains a Unity project, which allows to test the desktop-based visualization techniques introduced in the Paper "Been there, Seen that: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments". It allows you to analyze 3D gaze and movement data sets recorded using the HoloLens 2. It provides you with a gaze replay visualization, which is linked to a space-time cube visualization to show an overview of the behavioral data and inspect important events in more detail.The project includes a folder called Assets, which contains the necessary scripts and data. The project can be opened using Unity. We recommend Unity version 2020.3.24f. The scenes GazeReplay and STC need to be dragged into the hierarchy window and the GazeReplay scene must be unloaded. Afterward, the visualization can be viewed and tested within the game view by hitting the play button. Please check the GitHub page for the latest version. }, affiliation = {Pathmanathan, Nelusa/Universität Stuttgart, Öney, Seyda/Universität Stuttgart, Becher, Michael/Universität Stuttgart, Sedlmair, Michael/Universität Stuttgart, Weiskopf, Daniel/Universität Stuttgart, Kurzhals, Kuno/Universität Stuttgart}, author = {Pathmanathan, Nelusa and Öney, Seyda and Becher, Michael and Sedlmair, Michael and Weiskopf, Daniel and Kurzhals, Kuno}, doi = {10.18419/darus-3383}, howpublished = {Software}, note = {Related to: N. Pathmanathan, S. Öney, M. Becher, M. Sedlmair, D. Weiskopf, K. Kurzhals, "Been There, Seen That: Visualization of 3D eye tracking data from real world environments", Computer Graphics Forum, 2023, Under Review}, orcid-numbers = {Pathmanathan, Nelusa/0000-0002-6848-8554, Öney, Seyda/0000-0002-5785-6788, Becher, Michael/0000-0002-0072-1655, Sedlmair, Michael/0000-0001-7048-9292, Weiskopf, Daniel/0000-0003-1174-1026, Kurzhals, Kuno/0000-0003-4919-4582}, publisher = {DaRUS}, title = {Replication Data for: Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments}, year = 2023 }
2. Öney, S., Pathmanathan, N., Becher, M., Sedlmair, M., Weiskopf, D., & Kurzhals, K. (2023). Replication Data for: Visual Gaze Labeling for Augmented Reality Studies. DaRUS. https://doi.org/10.18419/darus-3384
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  @dataset{oney2023replication, abstract = {This .zip file contains the Unity project of the visualization tool described in the paper "Visual Gaze Labeling for Augmented Reality Studies" which was accepted at EuroVis 2023 Conference. This tool can be used to annotate gaze data from Augmented Reality (AR) scenarios to perform AOI based eye-tracking analysis. The visualization tool consists of a gaze replay and timeline visualization - linked together to provide spatial and image-based annotation. The project includes a dataset that we collected from an AR pilot study.Please install Unity 2020.3.24 to run the visualization tool. The Unity project contains two scenes (located in Assets/Scenes):TimelineVisualizationGazeReplayFirst, drag both scenes into the hierarchy window, then unload GazeReplay. In File/Build Settings, the order of the Scenes in Build should be as follows: TimelineVisualization 0, GazeReplay 1. When you start the active scene (TimelineVisualization), the GazeReplay scene will be loaded as well. You can work with the visualization tool in the game view.The project consists of an Assets, Packages and ProjectSettings folder. Please check the GitHub page for the latest version. }, affiliation = {Öney, Seyda/Universität Stuttgart, Pathmanathan, Nelusa/Universität Stuttgart, Becher, Michael/Universität Stuttgart, Sedlmair, Michael/Universität Stuttgart, Weiskopf, Daniel/Universität Stuttgart, Kurzhals, Kuno/Universität Stuttgart}, author = {Öney, Seyda and Pathmanathan, Nelusa and Becher, Michael and Sedlmair, Michael and Weiskopf, Daniel and Kurzhals, Kuno}, doi = {10.18419/darus-3384}, howpublished = {Software}, note = {Related to: Öney, Seyda, et al. "Visual Gaze Labeling for Augmented Reality Studies." Computer Graphics Forum. 2023 (under review)}, orcid-numbers = {Öney, Seyda/0000-0002-5785-6788, Pathmanathan, Nelusa/0000-0002-6848-8554, Becher, Michael/0000-0002-0072-1655, Sedlmair, Michael/0000-0001-7048-9292, Weiskopf, Daniel/0000-0003-1174-1026, Kurzhals, Kuno/0000-0003-4919-4582}, publisher = {DaRUS}, title = {Replication Data for: Visual Gaze Labeling for Augmented Reality Studies}, year = 2023 }
2022
1. Abdelaal, M., Schiele, N. D., Angerbauer, K., Kurzhals, K., Sedlmair, M., & Weiskopf, D. (2022). Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations. DaRUS. https://doi.org/10.18419/DARUS-3100
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  @dataset{https://doi.org/10.18419/darus-3100, author = {Abdelaal, Moataz and Schiele, Nathan Daniel and Angerbauer, Katrin and Kurzhals, Kuno and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.18419/DARUS-3100}, publisher = {DaRUS}, title = {Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3100}, year = 2022 }

Dr. Kuno Kurzhals

Independent Junior Research Group Leader

Phone: +49 711 685 88609

E-Mail