Ryan P. McMahan

Esteban Segarra Martinez, Ryan P. McMahan

Point clouds are a 3D space representation of an environment that was recorded with a high precision laser scanner. These scanners can suffer from environmental interference such as surface shading, texturing, and reflections. Because of this, point clouds may be contaminated with fake or incorrect colors. Current open source or proprietary tools offer limited or no access to correcting these visual errors automatically. RecolorCloud is a tool developed to resolve these color conflicts by utilizing automated color recoloring. We offer the ability to deleting or recoloring outlier points automatically with users only needing to specify bounding box regions to effect colors. Results show a vast improvement of the photo-realistic quality of large point clouds. Additionally, users can quickly recolor a point cloud with set semantic segmentation colors.

Alec G. Moore, Tiffany D. Do, Nayan N. Chawla et al.

In recent years, numerous researchers have begun investigating how virtual reality (VR) tracking and interaction data can be used for a variety of machine learning purposes, including user identification, predicting cybersickness, and estimating learning gains. One constraint for this research area is the dearth of open datasets. In this paper, we present a new open dataset captured with our VR-based Full-scale Assembly Simulation Testbed (FAST). This dataset consists of data collected from 108 participants (50 females, 56 males, 2 non-binary) learning how to assemble two distinct full-scale structures in VR. In addition to explaining how the dataset was collected and describing the data included, we discuss how the dataset may be used by future researchers.

Lukas Schach, Christian Rack, Ryan P. McMahan et al.

This paper examines the generalization capacity of two state-of-the-art classification and similarity learning models in reliably identifying users based on their motions in various Extended Reality (XR) applications. We developed a novel dataset containing a wide range of motion data from 49 users in five different XR applications: four XR games with distinct tasks and action patterns, and an additional social XR application with no predefined task sets. The dataset is used to evaluate the performance and, in particular, the generalization capacity of the two models across applications. Our results indicate that while the models can accurately identify individuals within the same application, their ability to identify users across different XR applications remains limited. Overall, our results provide insight into current models generalization capabilities and suitability as biometric methods for user verification and identification. The results also serve as a much-needed risk assessment of hazardous and unwanted user identification in XR and Metaverse applications. Our cross-application XR motion dataset and code are made available to the public to encourage similar research on the generalization of motion-based user identification in typical Metaverse application use cases.

Tiffany D. Do, Seong Ioi Wang, Dylan S. Yu et al.

League of Legends (LoL) is the most widely played multiplayer online battle arena (MOBA) game in the world. An important aspect of LoL is competitive ranked play, which utilizes a skill-based matchmaking system to form fair teams. However, players' skill levels vary widely depending on which champion, or hero, that they choose to play as. In this paper, we propose a method for predicting game outcomes in ranked LoL games based on players' experience with their selected champion. Using a deep neural network, we found that game outcomes can be predicted with 75.1% accuracy after all players have selected champions, which occurs before gameplay begins. Our results have important implications for playing LoL and matchmaking. Firstly, individual champion skill plays a significant role in the outcome of a match, regardless of team composition. Secondly, even after the skill-based matchmaking, there is still a wide variance in team skill before gameplay begins. Finally, players should only play champions that they have mastered, if they want to win games.

Tiffany D. Do, Joseph J. LaViola, Ryan P. McMahan

Depth perception of objects can greatly affect a user's experience of an augmented reality (AR) application. Many AR applications require depth matching of real and virtual objects and have the possibility to be influenced by depth cues. Color and luminance are depth cues that have been traditionally studied in two-dimensional (2D) objects. However, there is little research investigating how the properties of three-dimensional (3D) virtual objects interact with color and luminance to affect depth perception, despite the substantial use of 3D objects in visual applications. In this paper, we present the results of a paired comparison experiment that investigates the effects of object shape, fidelity, color, and luminance on depth perception of 3D objects in handheld mobile AR. The results of our study indicate that bright colors are perceived as nearer than dark colors for a high-fidelity, simple 3D object, regardless of hue. Additionally, bright red is perceived as nearer than any other color. These effects were not observed for a low-fidelity version of the simple object or for a more-complex 3D object. High-fidelity objects had more perceptual differences than low-fidelity objects, indicating that fidelity interacts with color and luminance to affect depth perception. These findings reveal how the properties of 3D models influence the effects of color and luminance on depth perception in handheld mobile AR and can help developers select colors for their applications.

Afshin Taghavi Nasrabadi, Aliehsan Samiei, Anahita Mahzari et al.

In this paper, we propose a taxonomy for 360° videos that categorizes videos based on moving objects and camera motion. We gathered and produced 28 videos based on the taxonomy, and recorded viewport traces from 60 participants watching the videos. In addition to the viewport traces, we provide the viewers' feedback on their experience watching the videos, and we also analyze viewport patterns on each category.

Asma Naz, Regis Kopper, Ryan P. McMahan et al.

The emotional response a person has to a living space is predominantly affected by light, color and texture as space-making elements. In order to verify whether this phenomenon could be replicated in a simulated environment, we conducted a user study in a six-sided projected immersive display that utilized equivalent design attributes of brightness, color and texture in order to assess to which extent the emotional response in a simulated environment is affected by the same parameters affecting real environments. Since emotional response depends upon the context, we evaluated the emotional responses of two groups of users: inactive (passive) and active (performing a typical daily activity). The results from the perceptual study generated data from which design principles for a virtual living space are articulated. Such a space, as an alternative to expensive built dwellings, could potentially support new, minimalist lifestyles of occupants, defined as the neo-nomads, aligned with their work experience in the digital domain through the generation of emotional experiences of spaces. Data from the experiments confirmed the hypothesis that perceivable emotional aspects of real-world spaces could be successfully generated through simulation of design attributes in the virtual space. The subjective response to the virtual space was consistent with corresponding responses from real-world color and brightness emotional perception. Our data could serve the virtual reality (VR) community in its attempt to conceive of further applications of virtual spaces for well-defined activities.