EyeSee3D

Pfeiffer, Thies; Renner, Patrick

doi:10.1145/2578153.2628814

Cited by 22 publications

(2 citation statements)

References 18 publications

(15 reference statements)

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“…Node‐link representations of scanpaths [GH10b] are also established means to show gaze data without aggregation. These techniques have been adapted to 3D environments [Pfe12,SND10a,PSF * 13] but as a depiction of gaze distributions of single participants. Achieving an aggregated visualization of gaze from multiple participants in the real‐world is not trivial and to the best of our knowledge, a comprehensive visualization technique for multi‐user comparison does not exist.…”

Section: Related Workmentioning

confidence: 99%

Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

Pathmanathan

Oney

Becher

et al. 2023

Computer Graphics Forum

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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.

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Section: Related Workmentioning

confidence: 99%

Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

Pathmanathan

Oney

Becher

et al. 2023

Computer Graphics Forum

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“…These manual methods are very effective and applicable to any possible case, but also highly tedious. Over the last few years, marker-based approaches using visible, infrared or natural markers have become more and more common and are now widely used for automated computing of AOIs (Kiefer, Giannopoulos, Kremer, Schlieder, & Raubal, 2014;Pfeiffer & Renner, 2014;Zhang, Zheng, Hong, & Mou, 2015). Although the use of markers can accelerate the evaluation process enormously, they are limited to the types of scenes that can be analyzed (Evans, Jacobs, Tarduno, & Pelz, 2012).…”

Section: Introductionmentioning

confidence: 99%

Automating areas of interest analysis in mobile eye tracking experiments based on machine learning

Wolf

Hess

Bachmann

et al. 2018

JEMR

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For an in-depth, AOI-based analysis of mobile eye tracking data, a preceding gaze assignment step is inevitable. Current solutions such as manual gaze mapping or marker-based approaches are tedious and not suitable for applications manipulating tangible objects. This makes mobile eye tracking studies with several hours of recording difficult to analyse quantitatively. We introduce a new machine learning-based algorithm, the computational Gaze-Object Mapping (cGOM), that automatically maps gaze data onto respective AOIs. cGOM extends state-of-the-art object detection and segmentation by mask R-CNN with a gaze mapping feature. The new algorithm’s performance is validated against a manual fixation-by-fixation mapping, which is considered as ground truth, in terms of true positive rate (TPR), true negative rate (TNR) and efficiency. Using only 72 training images with 264 labelled object representations, cGOM is able to reach a TPR of approx. 80% and a TNR of 85% compared to the manual mapping. The break-even point is reached at 2 hours of eye tracking recording for the total procedure, respectively 1 hour considering human working time only. Together with a real-time capability of the mapping process after completed training, even hours of eye tracking recording can be evaluated efficiently. (Code and video examples have been made available at: https://gitlab.ethz.ch/pdz/cgom.git)

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The realm of metaverse: A survey

Venugopal

Subramanian

Peatchimuthu

2023

Computer Animation & Virtual

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The next step of digital development is the metaverse, which has the potential to drastically alter how people use technology and expand the range of services available beyond conventional systems that can be accessed online. As the efficiency, performance, and quality of service access reach their peak levels, the focus has shifted to the user experience. Due to this, there is an increasing demand for more involved and thorough customer service, and service providers are willing to increase their present standards. Consumers are genuinely asking for tactile and immersive elements in their digital interfaces, but these features can only be made possible by the metaverse's potentially futuristic subfields of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR). However, the metaverse may not be widely used due to significant security and privacy issues either from underlying technology or produced by the new digital environment. A variety of fundamental problems, such as scalability and interoperability, can arise in terms of ensuring security for the metaverse because of the metaverse's inherent properties, such as immersive realism, sustainability, and heterogeneity. In this survey, we propose a hypothetical meta‐stack framework to understand the various components in the realm of metaverse and then provide wide‐ranging insights on the most recent development in metaverse realm in the context of cutting‐edge technologies, security vulnerabilities and preventive measures specific to the metaverse and the research challenges pertaining to metaverse.

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EyeSee3D

Cited by 22 publications

References 18 publications

Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

Automating areas of interest analysis in mobile eye tracking experiments based on machine learning

The realm of metaverse: A survey

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