Niklas Pfützenreuter

Academic Staff

Niklas Pfützenreuter, M.Sc.

S-M 204a
+49 201 18-33754


  • Liebers, Carina; Pfützenreuter, Niklas; Auda, Jonas; Gruenefeld, Uwe; Schneegass, Stefan: "Computer, Generate!” – Investigating User Controlled Generation of Immersive Virtual Environments. In: HHAI 2024: Hybrid Human AI Systems for the Social Good. IOS Press, Malmö 2024, p. 213-227. doi:10.3233/FAIA240196PDFFull textCitationDetails

    For immersive experiences such as virtual reality, explorable worlds are often fundamental. Generative artificial intelligence looks promising to accelerate the creation of such environments. However, it remains unclear how existing interaction modalities can support user-centered world generation and how users remain in control of the process. Thus, in this paper, we present a virtual reality application to generate virtual environments and compare three common interaction modalities (voice, controller, and hands) in a pre-study (N = 18), revealing a combination of initial voice input and continued controller manipulation as best suitable. We then investigate three levels of process control (all-at-once, creation-before-manipulation, and step-by-step) in a user study (N = 27). Our results show that although all-at-once reduced the number of object manipulations, participants felt more in control when using the step-by-step approach.

  • Liebers, Carina; Pfützenreuter, Niklas; Prochazka, Marvin; Megarajan, Pranav; Furuno, Eike; Löber, Jan; Stratmann, Tim C.; Auda, Jonas; Degraen, Donald; Gruenefeld, Uwe; Schneegass, Stefan: Look Over Here! Comparing Interaction Methods for User-Assisted Remote Scene Reconstruction. In: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA 2024. doi:10.1145/3613905.3650982PDFCitationDetails

    Detailed digital representations of physical scenes are key in many cases, such as historical site preservation or hazardous area inspection. To automate the capturing process, robots or drones mounted with sensors can algorithmically record the environment from different viewpoints. However, environmental complexities often lead to incomplete captures. We believe humans can support scene capture as their contextual understanding enables easy identification of missing areas and recording errors. Therefore, they need to perceive the recordings and suggest new sensor poses. In this work, we compare two human-centric approaches in Virtual Reality for scene reconstruction through the teleoperation of a remote robot arm, i.e., directly providing sensor poses (direct method) or specifying missing areas in the scans (indirect method). Our results show that directly providing sensor poses leads to higher efficiency and user experience. In future work, we aim to compare the quality of human assistance to automatic approaches.

  • Liebers, Carina; Prochazka, Marvin; Pfützenreuter, Niklas; Liebers, Jonathan; Auda, Jonas; Gruenefeld, Uwe; Schneegass, Stefan: Pointing It out! Comparing Manual Segmentation of 3D Point Clouds between Desktop, Tablet, and Virtual Reality. In: International Journal of Human–Computer Interaction (2023), p. 1-15. doi:10.1080/10447318.2023.2238945PDFFull textCitationDetails
    Pointing It out! Comparing Manual Segmentation of 3D Point Clouds between Desktop, Tablet, and Virtual Reality

    Scanning everyday objects with depth sensors is the state-of-the-art approach to generating point clouds for realistic 3D representations. However, the resulting point cloud data suffers from outliers and contains irrelevant data from neighboring objects. To obtain only the desired 3D representation, additional manual segmentation steps are required. In this paper, we compare three different technology classes as independent variables (desktop vs. tablet vs. virtual reality) in a within-subject user study (N = 18) to understand their effectiveness and efficiency for such segmentation tasks. We found that desktop and tablet still outperform virtual reality regarding task completion times, while we could not find a significant difference between them in the effectiveness of the segmentation. In the post hoc interviews, participants preferred the desktop due to its familiarity and temporal efficiency and virtual reality due to its given three-dimensional representation.