arXiv subjects

Julian Koch

Publications and source records attributed to Julian Koch.

7 recordsLinked to original sources

arXiv2026

Hot Wire 5D+: Evaluating Cognitive and Motor Trade-offs of Visual Feedback for 5D Augmented Reality Trajectories

Augmented Reality (AR) is increasingly utilized to guide users through complex spatial tasks in domains such as manufacturing, non-destructive testing, and surgery. These applications often require strict compliance with 5D+ trajectories using rotation-symmetric tools (3D position, 2D orientation, and movement speed). However, the sensori-motor baselines of untrained users during these multidimensional tracing tasks, along with the cognitive-motor trade-offs induced by varying visual feedback paradigms, remain underexplored. We present a controlled within-subjects user study (N=30) evaluating three distinct AR UI concepts for trajectory guidance, both with and without explicit orientation constraints. We analyzed spatial, orientational, and speed compliance based on the internal AR tracking, which was validated against a high-precision external optical tracking system to rule out hardware drift. By segmenting the execution into transient and steady-state phases and applying Aligned Rank Transform (ART) ANOVA, we isolated the interaction effects between visual design and task complexity. Alongside subjective metrics (NASA-TLX, SUS), our results establish conservative performance baselines for novice users performing freehand 5D trajectory following. We reveal orientation-induced cognitive-motor trade-offs and identify mitigating UI synergies. Ultimately, we provide empirical baselines and actionable design guidelines for developing effective AR guidance systems.

cs.HC↗

arXiv2026

Open-vocabulary 3D scene perception in industrial environments

Autonomous vision applications in production, intralogistics, or manufacturing environments require perception capabilities beyond a small, fixed set of classes. Recent open-vocabulary methods, leveraging 2D Vision-Language Foundation Models (VLFMs), target this task but often rely on class-agnostic segmentation models pre-trained on non-industrial datasets (e.g., household scenes). In this work, we first demonstrate that such models fail to generalize, performing poorly on common industrial objects. Therefore, we propose a training-free, open-vocabulary 3D perception pipeline that overcomes this limitation. Instead of using a pre-trained model to generate instance proposals, our method simply generates masks by merging pre-computed superpoints based on their semantic features. Following, we evaluate the domain-adapted VLFM "IndustrialCLIP" on a representative 3D industrial workshop scene for open-vocabulary querying. Our qualitative results demonstrate successful segmentation of industrial objects.

cs.CV↗

arXiv2025

Room acoustics affect communicative success in hybrid meeting spaces: a pilot study

Since the COVID-19 pandemic in 2020, universities and companies have increasingly integrated hybrid features into their meeting spaces, or even created dedicated rooms for this purpose. While the importance of a fast and stable internet connection is often prioritized, the acoustic design of seminar rooms is frequently overlooked. Poor acoustics, particularly excessive reverberation, can lead to issues such as misunderstandings, reduced speech intelligibility or cognitive and vocal fatigue. This pilot study investigates whether room acoustic interventions in a seminar room at Graz University of Technology support better communication in hybrid meetings. For this purpose, we recorded two groups of persons twice, once before and once after improving the acoustics of the room. Our findings -- despite not reaching statistical significance due to the small sample size - indicate clearly that our spatial interventions improve communicative success in hybrid meetings. To make the paper accessible also for readers from the speech communication community, we explain room acoustics background, relevant for the interpretation of our results.

cs.CL↗

arXiv2025

Evaluating Magic Leap 2 Tool Tracking for AR Sensor Guidance in Industrial Inspections

Rigorous evaluation of commercial Augmented Reality (AR) hardware is crucial, yet public benchmarks for tool tracking on modern Head-Mounted Displays (HMDs) are limited. This paper addresses this gap by systematically assessing the Magic Leap 2 (ML2) controllers tracking performance. Using a robotic arm for repeatable motion (EN ISO 9283) and an optical tracking system as ground truth, our protocol evaluates static and dynamic performance under various conditions, including realistic paths from a hydrogen leak inspection use case. The results provide a quantitative baseline of the ML2 controller's accuracy and repeatability and present a robust, transferable evaluation methodology. The findings provide a basis to assess the controllers suitability for the inspection use case and similar industrial sensor-based AR guidance tasks.

cs.RO↗

arXiv2025

Homogeneous doping of epitaxial graphene by Pb(111) islands: A magnetotransport study

Proximity coupling is an effective approach for the functionalization of graphene. However, graphene's inertness inhibits the adsorption of closed films, thus favoring island growth, whose inhomogeneity might be reflected in the induced properties. In order to study the homogeneity of the doping profile induced by an inhomogeneous coverage and the spin orbit coupling (SOC) induced in graphene, we deposited Pb(111) islands with an average coverage of up to 30 ML on monolayer graphene (MLG) on SiC(0001) at room temperature (RT). We investigated the transport properties and the structure using magnetotransport, and scanning tunneling microscopy and low energy electron deflection, respectively. The Pb(111) islands act as donors, increasing the electron concentration of graphene by about $5\times10^{11}\;\text{ML}^{-1}\text{cm}^{-2}$. The doping was found to be homogeneous, in stark contrast to our previous results for Bi islands on MLG. Upon percolation of the Pb layer at around 5 ML, hole transport through the Pb islands has to be taken into account in order to describe the transport data. The Pb(111) islands do not induce any Rashba SOC, contrary to theoretical predictions for an interface between Pb(111) and graphene. Moreover, they seem to screen the defects in the graphene, resulting in a reduction of the intervalley scattering rate up to 5 ML.

cond-mat.mes-hall↗

arXiv2024

Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings

In recent years, the upstream of Large Language Models (LLM) has also encouraged the computer vision community to work on substantial multimodal datasets and train models on a scale in a self-/semi-supervised manner, resulting in Vision Foundation Models (VFM), as, e.g., Contrastive Language-Image Pre-training (CLIP). The models generalize well and perform outstandingly on everyday objects or scenes, even on downstream tasks, tasks the model has not been trained on, while the application in specialized domains, as in an industrial context, is still an open research question. Here, fine-tuning the models or transfer learning on domain-specific data is unavoidable when objecting to adequate performance. In this work, we, on the one hand, introduce a pipeline to generate the Industrial Language-Image Dataset (ILID) based on web-crawled data; on the other hand, we demonstrate effective self-supervised transfer learning and discussing downstream tasks after training on the cheaply acquired ILID, which does not necessitate human labeling or intervention. With the proposed approach, we contribute by transferring approaches from state-of-the-art research around foundation models, transfer learning strategies, and applications to the industrial domain.

cs.CV↗

arXiv2023

Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge.

cs.CV↗