Integrating augmented reality (AR) into industrial assembly processes has great potential, yet most studies use simplified lab tasks and rarely benchmark AR against commercial systems, limiting their industrial relevance. To address this gap, we present a comparative study of immersive AR and non-immersive touchscreen-based assembly support systems on a manufacturing shop floor, with 16 participants assembling two industry-grade assets of different complexity. The results reveal that while AR's immersive capabilities excelled in spatial guidance for complex tasks, the simplicity and reliability of the touchscreen interface proved more effective for simple assemblies. We derive three design implications for deployment practice.

Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural networks to map pixel coordinates to RGB values, capturing shapes, colors, and textures within the network’s weights. Recently, GaussianImage has been proposed as an alternative, using Gaussian functions instead of neural networks to achieve comparable quality and compression. Such a solution obtains a quality and compression ratio similar to classical INR models but does not allow image modification. In contrast, our work introduces a novel method, MiraGe, which uses mirror reflections to perceive 2D images in 3D space and employs flat-controlled Gaussians for precise 2D image editing. Our approach improves the rendering quality and allows realistic image modifications, including human-inspired perception of photos in the 3D world. Thanks to modeling images in 3D space, we obtain the illusion of 3D-based modification in 2D images. We also show that our Gaussian representation can be easily combined with a physics engine to produce physics-based modification of 2D images. Consequently, MiraGe allows for better quality than the standard approach and natural modification of 2D images.

A key problem in the adoption of exoskeletons in industry is that workers are incorrectly fitting the device, leading to discomfort and suboptimal functioning of the exoskeleton. Although biomechanical modeling and design optimization strategies have tried to resolve this issue, we propose a user-centric, real-time fitting aid to guide and control the correct fitting process, which has not been achieved in practice. Inspired by previous work that uses exergame-like feedback to instruct a user, we compared augmented reality (AR)-based visual cues to guide the accurate fitting of a passive upper limb exoskeleton. We selected visual cues through a systematic literature review and evaluated their efficacy and usability for different aspects of exoskeleton fitting in a study with sixteen participants. The study outcome suggests a statistically significant preference for a semi-transparent overlay instead of a more abstract arrow-based method. Moreover, the results indicate high usability and satisfaction with our approach, improved user acceptance, and potentially enhanced fitting accuracy. These findings advance understanding of the viability of exergame-like real-time guidance as a means to increase exoskeleton acceptance and adoption in industrial settings.

The successful deployment of augmented reality (AR) in the industry for on-the-job guidance depends heavily on factors such as the availability of required expertise, existing digital content and other deployment-related criteria such as a task's error-proneness or complexity. Particularly in idiosyncratic manufacturing situations involving customised products and diverse complex and non-complex products and its variants, the applicability and attractiveness of AR as a worker assistance system is often unclear and difficult to gauge for decision-makers. To address this gap, we developed a decision support tool to help prepare customised deployment strategies for AR-based assistance systems utilising manual assembly as the main example. Consequently, we report results from an interview study with sixteen domain experts. Furthermore, when analysing captured expert knowledge, we found significant differences in criteria weighting based on task complexity and other factors, such as the effort required to obtain data.

Collaborative robots (cobots) are a promising technology for frontline workers in industry. They can support tasks that cannot be fully automated but are repetitive, fatiguing, boring, or dangerous for humans. Although cobots are explicitly designed to work with humans, they remain primarily non-intuitive and difficult to collaborate with. Thus, there is a need for new interaction approaches to facilitate efficient human-robot collaboration. Recently, we could see emerging examples of using augmented reality (AR) to assist a worker in collaborative task execution with a cobot. However, for such an approach to provide truly efficient support for the seamless bimanual task execution, we need to first investigate interaction methods offered by an AR interface. To that end, we performed a study with sixteen participants to compare eye-gaze and hand-ray pointing methods for part selection in collaborative, manual assembly tasks. The results of our study show that both techniques provide similar perceived usability, with the eye-gaze selection leading to significantly shorter completion times.

Over the past years, we have observed an abundance of approaches for modeling dynamic 3D scenes using Gaussian Splatting (GS). These solutions use GS to represent the scene's structure and the neural network to model dynamics. Such approaches allow fast rendering and extracting each element of such a dynamic scene. However, modifying such objects over time is challenging. SC-GS (Sparse Controlled Gaussian Splatting) enhanced with Deformed Control Points partially solves this issue. However, this approach necessitates selecting elements that need to be kept fixed, as well as centroids that should be adjusted throughout editing. Moreover, this task poses additional difficulties regarding the re-productivity of such editing. To address this, we propose Dynamic Multi-Gaussian Soup (D-MiSo), which allows us to model the mesh-inspired representation of dynamic GS. Additionally, we propose a strategy of linking parameterized Gaussian splats, forming a Triangle Soup with the estimated mesh. Consequently, we can separately construct new trajectories for the 3D objects composing the scene. Thus, we can make the scene's dynamic editable over time or while maintaining partial dynamics.

The primary goal behind the system presented in this paper is to investigate the efficacy of using virtual reality (VR) for showcasing sizable medical equipment. Specifically, we focused on a mobile magnetic resonance imaging (MRI) scanner mounted on a truck trailer. The latter is integral to the mobile MRI setup and must be presented as part of the immersive experience. Therefore, we not only have to depict the medical apparatus but also provide the means of understanding its surroundings. This is especially important to radiologists and other medical personnel to ascertain if a given mobile medical facility fulfills their needs and wants. Furthermore, despite such MRI devices being designed for mobility, their long-distance transportation can be time-consuming, troublesome and expensive. Therefore, we can observe the need for showcasing such mobile MRI units without additional cost and burden related to transportation. To achieve this, we designed an immersive environment in which the users can interact with the real-life scale 3D model of a mobile MRI. In addition, we also verified the usability and expressiveness of our system using established heuristical approaches.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

In recent years, additive manufacturing (AM) techniques have transcended their typical rapid prototyping role and become viable methods to directly manufacture end products in a highly versatile manner. Due to its low cost and relative ease of use, fused deposition modeling (FDM) has become the most universally applied AM technology. Nonetheless, skilled operators are often still required to perform maintenance, diagnostic, and repair tasks. Such operators need to be adequately trained. Here, Augmented reality (AR) technology could be used to automate this training and help to promptly provide new operators with the necessary skills to perform specific tasks as required. However, the most effective approach to designing such AR-based assistance systems has not yet been fully explored. Consequently, we address this need by reporting on how to design such guiding systems using well-known design engineering methodologies. We then further assess the applicability of our approach through a user study with domain experts. In addition, we complete our assessment with heuristical verification of system expressiveness to reason about the influence of cognitively important components of the AR interface on the operators.

Designing a 3-D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves the synthesis and coloring of 3-D models within the scene. To lessen this workload, we can apply machine learning to automate some aspects of the game scene development. Earlier research has already tackled automated generation of the game scene background with machine learning. However, model autocoloring remains an underexplored problem. The automatic coloring of a 3-D model is a challenging task, especially when dealing with the digital representation of a colorful, multipart object. In such a case, we have to “understand” the object's composition and coloring scheme of each part. Moreover, existing single-stage methods have their caveats. We address these limitations by proposing a two-stage training approach to synthesize autocolored 3-D models. In the first stage, we obtain a 3-D point cloud representing a 3-D object, while in the second stage, we assign colors to points within such a cloud. Next, we generate a 3-D mesh in which the surfaces are colored based on the interpolation of colored points representing vertices of a given mesh triangle. This approach allows us to develop a smooth coloring scheme.

In this work, we assess the classification capability of visualized multidimensional data used in the decision- making process. We want to investigate if classification carried out over a graphical representation of the tabular data allows for statistically greater efficiency than the dummy classifier method. To achieve this, we have used a convolutional neural network (ConvNet) as the base classifier. As an input into this model, we used data presented in the form of 2D curves resulting from the Parallel Coordinates Plot (PCP) visualization. Our initial results show that the topological arrangement of attributes, i.e., the shape formed by the PCP curves of individual data items, can serve as an effective classifier. Tests performed on three different real-world datasets from the UCI Machine Learning Repository confirmed that classification efficiency is significantly higher than in the case of dummy classification. The new method provides an interesting approach to the classificatio

Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlusions. Therefore, completing such partial representations remains a fundamental challenge of many computer vision applications. Most of the existing approaches aim to solve this problem by learning to reconstruct individual 3D objects in a synthetic setup of an uncluttered environment, which is far from a real-life scenario. In this work, we reformulate the problem of point cloud completion into an objects hallucination task. Thus, we introduce a novel autoencoder-based architecture called HyperPocket that disentangles latent representations and, as a result, enables the generation of multiple variants of the completed 3D point clouds. Furthermore, we split point cloud processing into two disjoint data streams and leverage a hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the missing object parts. As a result, the generated point clouds are smooth, plausible, and geometrically consistent with the scene. Moreover, our method offers competitive performances to the other state-of-the-art models, enabling a plethora of novel applications.

Virtual Reality (VR) is a promising technology platform for immersive visual analytics. However, the design space of VR analytics interface design is vast and difficult to explore using traditional A/B comparisons in formal or informal controlled experiments— a fundamental part of an iterative design process. A key factor that complicates such comparisons is the dataset. Exposing participants to the same dataset in all conditions introduces an unavoidable learning effect. On the other hand, using different datasets for all experimental conditions introduces the dataset itself as an uncontrolled variable, which reduces internal validity to an unacceptable degree. In this paper, we propose to rectify this problem by introducing a generative process for synthesizing clustered datasets for VR analytics experiments. This process generates datasets that are distinct while simultaneously allowing systematic comparisons in experiments. A key advantage is that these datasets can then be used in iterative design processes. In a two-part experiment, we show the validity of the generative process and demonstrate how new insights in VR-based visual analytics can be gained using synthetic datasets.

Abstract Photogrammetry is a promising set of methods for generating photorealistic 3D models of physical objects and structures. Such methods may rely solely on camera-captured photographs or include additional sensor data. Digital twins are digital replicas of physical objects and structures. Photogrammetry is an opportune approach for generating 3D models for the purpose of preparing digital twins. At a sufficiently high level of quality, digital twins provide effective archival representations of physical objects and structures and become effective substitutes for engineering inspections and surveying. While photogrammetric techniques are well-established, insights about effective methods for interacting with such models in virtual reality remain underexplored. We report the results of a qualitative engineering case study in which we asked six domain experts to carry out engineering measurement tasks in an immersive environment using bimanual gestural input coupled with gaze-tracking. The qualitative case study revealed that gaze-supported bimanual interaction of photogrammetric 3D models is a promising modality for domain experts. It allows the experts to efficiently manipulate and measure elements of the 3D model. To better allow designers to support this modality, we report design implications distilled from the feedback from the domain experts.