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REVIEW 3 major objections 5 minor 52 references

A SLAM-based deployment pipeline can realize coherent large-scale MR art exhibitions, where spatial alignment functions as a curatorial design decision rather than a merely technical choice.

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2026-08-01 17:20 UTC pith:TTQHPWKV

load-bearing objection A real large-scale MR exhibition deployment with a genuinely useful pilot comparison, but the formal evaluation is single-arm and can't carry the causal claims about the pipeline; worth reviewing with required revisions. the 3 major comments →

arxiv 2607.17665 v1 pith:TTQHPWKV submitted 2026-07-20 cs.MM cs.HC

Toward Site-Aware MR Art Exhibitions: A SLAM-Based Deployment Pipeline for Spatial Coherence and Exhibition Experience

Yawei Zhao , Yuming Zhu , Hao Li , Yuqi Liang , Ao Yu , Anca-Simona Horvath , Pan Hui This is my paper
classification cs.MM cs.HC
keywords Mixed RealityMR Art ExhibitionSpatial AlignmentSLAMDeployment PipelineExhibition CoherenceUser ExperienceMarkerless Tracking
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper argues that the choice of how virtual content is anchored to physical space in mixed-reality art exhibitions is a design decision, not merely a technical one. Through a pilot study, it finds that SLAM-based markerless alignment supports more presence, immersion, and continuity than marker-based alignment in an exhibition setting. Building on that, the authors develop a deployment pipeline that combines scene reconstruction, runtime localization and anchoring, and curatorial planning, and use it to deploy a 26,000-square-meter MR exhibition. System metrics and 30 participants' feedback indicate stable performance and perceived coherence, with visitors remembering artworks as fused with their locations. The upshot is a repeatable, empirically grounded workflow for site-aware MR exhibitions.

Core claim

The central claim is that a SLAM-based deployment pipeline can support coherent MR exhibition realization at large scale, and that spatial alignment method choice shapes exhibition coherence, continuity, immersion, and artwork interpretation. The pilot study (N=13) reports that SLAM outperformed markers on presence, natural integration, ease, efficiency, excitement, and preference; the formal deployment (N=30, 26,000 m², 31 artworks) maintained 60–80 FPS with ~20 ms frame times and elicited positive NASA-TLX, UEQ, and SUS scores. Interviews suggest visitors experienced artworks as site-integrated and remembered content and location as a unit, which the authors attribute to the coordination o

What carries the argument

Simultaneous Localization and Mapping (SLAM): a markerless tracking method that continuously estimates the headset's position and orientation against the environment's features, enabling virtual artworks to stay anchored without visible markers. In this paper SLAM carries the argument by enabling continuous, uninterrupted spatial alignment, which the pilot study links to presence and immersion. Around it, the pipeline adds physical scene reconstruction (scanning and meshing the site), runtime pose localization and artwork anchoring, and a parallel curatorial track (thematic formation, narrative-aligned zoning, artwork–place matching) that decides where each artwork goes.

Load-bearing premise

The paper assumes that the SLAM advantage measured in a 13-person pilot in a simulated 1,800 m² space carries over to the real 26,000 m² deployment, and that the positive user feedback in the formal study reflects the pipeline's design rather than the novelty of MR or the curated artworks.

What would settle it

A controlled large-scale study with both marker-based and SLAM-based conditions, measuring tracking drift (e.g., pose error over distance walked) and visitor experience, would settle it. If marker-based alignment at scale produces equivalent coherence scores, or if SLAM drift exceeds the threshold where artworks visibly shift, the core claim fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Markerless SLAM alignment removes the scanning-for-codes interruptions that fragment the viewing experience in marker-based MR exhibitions.
  • A repeatable pipeline now exists for translating curatorial themes and site characteristics into a deployed large-scale MR exhibition.
  • Artworks placed with spatial and narrative fit are remembered together with their location, suggesting site integration is part of the artwork's expression.
  • System overhead stays within comfortable bounds even at 26,000 m² with 21 anchored artworks, indicating scalability for longer or larger exhibitions.
  • For curators, alignment method selection should be weighed as an experiential design choice, not just an implementation detail.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the content–location memory synergy holds, MR exhibitions could function as spatial mnemonics: a testable extension would compare recall of artwork content between site-matched and site-arbitrary placements.
  • The pipeline's reliance on the pilot study's short-term advantage suggests an open question: how SLAM handles low-texture, dynamic, or outdoor environments, which could be probed with a quantitative drift measurement over long sessions.
  • The curatorial coordination idea extends beyond art: the same staged pipeline could guide heritage walks, science exhibits, or wayfinding narratives in public spaces.
  • Because the formal evaluation lacked a marker-based control, a direct replication with both conditions at large scale would test whether the experiential advantage is due to alignment method or to the novelty/curation of the exhibition.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes a practical pipeline for deploying large-scale mixed-reality art exhibitions using SLAM-based spatial alignment. A pilot study (N=13) compares marker-based and SLAM-based alignment in a simulated 1,800 m² exhibition, reports better presence/immersion and usability for SLAM, and motivates the choice of SLAM for the pipeline. A formal study (N=30) deploys the pipeline in a real 26,000 m² exhibition, collects system telemetry (FPS, frame time, CPU time, memory) and self-reported user experience (NASA-TLX, UEQ, SUS), plus 18 semi-structured interviews. The authors claim that spatial alignment is not just a technical choice but a design decision that shapes exhibition coherence, continuity, immersion, and artwork interpretation, and that the pipeline provides a coordinated technical–curatorial workflow.

Significance. If the results hold, the paper offers one of the few documented end-to-end workflows for large-scale MR exhibitions, with a real deployment and a mixed-methods evaluation. Strengths include the direct pilot comparison of two alignment methods, the unusually large deployment area, objective system-overhead telemetry, and transparent reporting of interview themes. The pipeline description is concrete enough to be partially reproduced. However, the central experiential claims rest on a single-arm formal study with no comparison or control, and the pilot-to-deployment scale extrapolation is untested. These gaps substantially limit the causal attribution of the reported user experience to the SLAM-based pipeline.

major comments (3)
  1. [Section 5.1/5.2, Table 3] The formal evaluation is single-arm: participants experienced only the full SLAM-based pipeline, with no marker-based comparison, no non-MR control, and no counterbalanced condition. Positive NASA-TLX, UEQ, and SUS medians and the interview themes under ‘Artwork-space integration’ and ‘Content-location synergy’ could plausibly reflect the novelty of MR, the curated content, or the guided tour rather than the SLAM-based pipeline or spatial alignment as such. Because RQ3 and the central claim in Section 6.3 attribute these experiential outcomes to the pipeline, this missing baseline is load-bearing. The authors should either add a comparison condition, or substantially soften the causal language and explicitly frame the study as a feasibility demonstration.
  2. [Section 3.1/3.2, Tables 1–2] The pilot study reports about twenty Wilcoxon signed-rank tests on item-level Likert scores with N=13, and no correction for multiple comparisons is applied. Several p-values are 0.01–0.05, which are fragile at this sample size. The qualitative data partially supports the SLAM advantage, but the statistical claims in Table 1 are over-stated as ‘significantly better’. Report effect sizes and adjusted p-values, or clearly label the pilot as exploratory. The method-selection decision based on this pilot is central to the rest of the paper, so this affects the evidentiary foundation.
  3. [Section 5.2, Figure 4] The system overhead metrics (FPS, frame time, CPU time, memory) demonstrate runtime stability, but they do not measure spatial alignment quality—there is no tracking-error, drift, re-localization success, or localization-latency data at the 26,000 m² scale. Thus the claim that the SLAM-based method’s advantage observed in the 1,800 m² pilot persists at deployment scale is untested. SLAM drift, server-localization latency, and environmental variability could neutralize the pilot’s advantage. Please add quantitative alignment-accuracy measurements at deployment scale, or clearly acknowledge this as a limitation.
minor comments (5)
  1. [Section 4, Figure 4 caption] The Figure 4 caption appears to be followed by a long sequence of garbled placeholder text (‘/uni00000013/uni00000015/...’). This makes the caption unreadable in the submitted PDF and should be fixed.
  2. [Section 5.2, Table 3] The NASA-TLX items are reverse-scored and reported with 5 as ‘optimal’, which inverts the standard NASA-TLX interpretation where higher workload is worse. The text should clarify how the original Likert scale was anchored and how reversal was applied, since readers may otherwise misinterpret the numerical values.
  3. [Section 7] The Limitations paragraph acknowledges participant representativeness and limited interaction design, but does not mention the absence of a baseline or comparison condition in the formal study. Given the central causal claims, this omission should be corrected.
  4. [Section 3.2] There are minor typos: ‘form the visitor’s perspective’ should be ‘from the visitor’s perspective’, and ‘form the organizer’s aspect’ should be ‘from the organizer’s aspect’. Similar phrasing appears elsewhere.
  5. [References] Several references omit venue or journal identifiers (e.g., [8], [16], [46]). Please ensure all entries are complete per the conference reference format.

Circularity Check

0 steps flagged

No circular derivation: the pilot directly compares two spatial-alignment methods and the pipeline evaluation is empirical, not reduced to its inputs.

full rationale

This paper's central chain is: (1) a within-subjects pilot study compares marker-based and SLAM-based alignment in an MR exhibition with identical content and layouts (Section 3.1); (2) based on statistical and qualitative results, SLAM is selected (Section 3.2); (3) a deployment pipeline is built around that choice (Section 4); and (4) the deployed system is evaluated via telemetry and questionnaires (Section 5). No equation is derived, and no parameter is fitted to a subset and then relabeled as a prediction. The pilot's conclusion is an empirical comparison between two conditions, not a construction that forces the subsequent claim. The formal study lacks a marker-based or no-MR control condition, so the positive NASA-TLX, UEQ, and SUS scores and interview themes cannot uniquely be attributed to the SLAM pipeline; however, that is an external-validity / attribution limitation, not circularity. Section 7 acknowledges participant representativeness and limited interaction design but omits the absence of a baseline comparison, which is worth flagging as a missing limitation, but omission of a limitation is not a circular step. There are also no load-bearing self-citations: the references are to external prior work, not the authors' own results, and no uniqueness theorem or prior-work ansatz is imported to forbid alternatives. The paper's contribution is largely procedural and evaluative rather than derivational, so no step reduces to its own input by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 5 axioms · 0 invented entities

No numerical parameters are fitted; the paper makes no quantitative derivation. The central claims rest on assumptions about measurement validity, sample representativeness, transferability of the pilot, and the reliability of qualitative coding. These are the 'axioms' that the empirical conclusions depend on.

axioms (5)
  • domain assumption The adapted IPQ, UEQ, NASA-TLX, and SUS questionnaires measure the intended constructs (presence, immersion, workload, usability) in this MR exhibition context.
    Sections 3.1 and 5.1 administer these instruments without context-specific validation; Cronbach's alpha >0.7 is cited as sufficient.
  • domain assumption Wilcoxon signed-rank tests applied item-by-item without multiple-comparison correction are an acceptable inferential procedure for the pilot questionnaire.
    Section 3.2 reports many significance tests at alpha=0.05; no adjustment or pre-registration is described.
  • domain assumption The pilot result (N=13, 1,800 m² simulated venue) generalizes to the 26,000 m² deployed exhibition.
    Section 4 selects SLAM based on the pilot; the formal study does not re-test the marker condition, so transferability is assumed.
  • domain assumption Spatial stability can be inferred from the four telemetry metrics (FPS, frame time, CPU time, memory), and 60-80 FPS implies good experiential continuity.
    Section 5.2 interprets system overhead as supporting immersion; no direct link between telemetry and experience is established.
  • domain assumption Thematic analysis of 18 semi-structured interviews yields reliable evidence about artwork interpretation and exhibition coherence.
    Section 5.1 describes a two-coder process but reports no intercoder reliability statistic; interpretation claims are based on selected quotes.

pith-pipeline@v1.3.0-alltime-deepseek · 16692 in / 13388 out tokens · 126761 ms · 2026-08-01T17:20:25.335825+00:00 · methodology

0 comments
read the original abstract

Mixed Reality (MR) is increasingly being used in exhibition settings to bring digital artworks into relation with the physical environment. However, existing MR exhibition systems are often confined to prototypes or case-specific deployments, offering limited guidance for large-scale practical implementation. To address this gap, this paper presents a practical pipeline for designing and deploying large-scale MR art exhibitions, treating spatial alignment not only as a technical mechanism but also as an experiential design decision. We first conducted a pilot study comparing marker-based and Simultaneous Localization and Mapping (SLAM)-based alignment methods in an MR exhibition setting. Based on the results, we developed a SLAM-based pipeline for MR exhibitions that integrates technical deployment with exhibition curation. We then evaluated the pipeline through both system overhead measures and users' experiential feedback. The results show that spatial alignment influences not only technical stability, but also overall exhibition coherence, visitors' sense of continuity and immersion, and artwork interpretation. These findings provide an empirically grounded reference for future large-scale MR art exhibition deployment.

Figures

Figures reproduced from arXiv: 2607.17665 by Anca-Simona Horvath, Ao Yu, Hao Li, Pan Hui, Yawei Zhao, Yuming Zhu, Yuqi Liang.

Figure 1
Figure 1. Figure 1: Pipeline steps of physical scene construction and [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: The layout of exhibition zones and representative [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The temporal distribution of metrics to evaluate the system overhead of the proposed pipeline. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

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Reference graph

Works this paper leans on

52 extracted references · 15 canonical work pages · 1 internal anchor

  1. [1]

    Hamza A Al-Jundi and Emad Y Tanbour. 2022. A framework for fidelity evaluation of immersive virtual reality systems.Virtual Reality26, 3 (2022), 1103–1122

    2022

  2. [2]

    Muazzam Artikova and Muzaffar Artikov. 2024. Augmented Reality Technologies in Mobile Applications To Optimize User Experience In Museum Tours. In2024 International Conference on Advanced Information Scientific Development (ICAISD) (Jawa Barat, Indonesia, 2024-11-25). IEEE, 31–34. doi:10.1109/ICAISD63055.2024. 10895520

    arXiv 2024

  3. [3]

    McMeekin, and Hafizur Rahaman

    Mafkereseb Kassahun Bekele, Erik Champion, David A. McMeekin, and Hafizur Rahaman. 2021. The Influence of Collaborative and Multi-Modal Mixed Reality: Cultural Learning in Virtual Heritage. 5, 12 (2021), 79. doi:10.3390/mti5120079

    work page doi:10.3390/mti5120079 2021

  4. [4]

    Mafkereseb Kassahun Bekele, Roberto Pierdicca, Emanuele Frontoni, Eva Savina Malinverni, and James Gain. 2018. A Survey of Augmented, Virtual, and Mixed Reality for Cultural Heritage. 11, 2 (2018), 7:1–7:36. doi:10.1145/3145534

    doi:10.1145/3145534 2018

  5. [5]

    2026.Blender Manual

    Blender Documentation Team. 2026.Blender Manual. https://docs.blender.org/ manual/en/latest/index.html

    2026

  6. [6]

    E. Bogle. 2013.Museum Exhibition Planning and Design. Bloomsbury Publishing. https://books.google.com/books?id=OR_9AAAAQBAJ

    2013

  7. [7]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology3, 2 (2006), 77–101

    2006

  8. [8]

    Peng Chen, Xinyu Zhao, Lina Zeng, Luxinyu Liu, Shengjie Liu, Li Sun, Zaijin Li, Hao Chen, Guojun Liu, Zhongliang Qiao, Yi Qu, Dongxin Xu, Lianhe Li, and Lin Li. 2025. A Review of Research on SLAM Technology Based on the Fusion of LiDAR and Vision. 25, 5 (2025). doi:10.3390/s25051447

    work page doi:10.3390/s25051447 2025

  9. [9]

    James J Cummings and Jeremy N Bailenson. 2016. How immersive is enough? A meta-analysis of the effect of immersive technology on user presence.Media psychology19, 2 (2016), 272–309

    2016

  10. [10]

    Areti Damala, Pierre Cubaud, Anne Bationo, Pascal Houlier, and Isabelle Marchal

  11. [11]

    Virginia A Dressler and Koon-Hwee Kan. 2018. Mediating museum display and technology: A case study of an international exhibition incorporating QR codes. Journal of Museum Education43, 2 (2018), 159–170

    2018

  12. [12]

    2024.Discovering statistics using IBM SPSS statistics

    Andy Field. 2024.Discovering statistics using IBM SPSS statistics. Sage publications limited

    2024

  13. [13]

    Garvin Goepel, George Guida, and Ana Gabriela Loayza Nolasco. 2023. Towards Hyper-Reality – A Case Study Mixed Reality Art Installation, Vol. 1. CAADRIA, 383–392. doi:10.52842/conf.caadria.2023.1.383

    work page doi:10.52842/conf.caadria.2023.1.383 2023

  14. [14]

    Ramy Hammady and Minhua Ma. 2021. Interactive Mixed Reality Technology for Boosting the Level of Museum Engagement. InAugmented Reality and Virtual Reality, M. Claudia Tom Dieck, Timothy H. Jung, and Sandra M. C. Loureiro (Eds.). Springer International Publishing, 77–91. doi:10.1007/978-3-030-68086-2_7

    work page doi:10.1007/978-3-030-68086-2_7 2021

  15. [15]

    Ramy Hammady, Minhua Ma, and Carl Strathearn. 2020. Ambient Information Visualisation and Visitors’ Technology Acceptance of Mixed Reality in Museums. 13, 2 (2020), 1–22. doi:10.1145/3359590

    work page doi:10.1145/3359590 2020

  16. [16]

    Mathieu Labbé and François Michaud. 2022. Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments. Volume 9 - 2022 (2022). doi:10.3389/frobt.2022.801886

    arXiv 2022

  17. [17]

    Bettina Laugwitz, Theo Held, and Martin Schrepp. 2008. Construction and evaluation of a user experience questionnaire. InSymposium of the Austrian HCI and usability engineering group. Springer, 63–76

    2008

  18. [18]

    James R Lewis. 2018. The system usability scale: past, present, and future.Inter- national Journal of Human–Computer Interaction34, 7 (2018), 577–590

    2018

  19. [19]

    Rong-Hao Liang, Hannah Van Iterson, Holly Krueger, Marina Toeters, and Loe Feijs. 2024. Chic-Marker: Fashionably Fusing Fiducial Markers into Apparel and Accessories. InProceedings of the 9th ACM Symposium on Computational Fabrication(Aarhus Denmark, 2024-07-07). ACM, 1–15. doi:10.1145/3639473. 3665790

    work page doi:10.1145/3639473 2024

  20. [20]

    2017.Augmented reality for developers: Build practical augmented reality applications with unity, ARCore, ARKit, and Vuforia

    Jonathan Linowes and Krystian Babilinski. 2017.Augmented reality for developers: Build practical augmented reality applications with unity, ARCore, ARKit, and Vuforia. Packt Publishing Ltd

    2017

  21. [21]

    Andréa Macario Barros, Maugan Michel, Yoann Moline, Gwenolé Corre, and Frédérick Carrel. 2022. A Comprehensive Survey of Visual SLAM Algorithms. 11, 1 (2022), 24. doi:10.3390/robotics11010024

    doi:10.3390/robotics11010024 2022

  22. [22]

    Mark McGill, Jan Gugenheimer, and Euan Freeman. 2020. A quest for co-located mixed reality: Aligning and assessing slam tracking for same-space multi-user experiences. InProceedings of the 26th ACM Symposium on Virtual Reality Software and Technology. 1–10

    2020

  23. [23]

    Vasiliki Nikolakopoulou, Spyros Vosinakis, Giorgos Nikopoulos, Modestos Stavrakis, Nikolaos Politopoulos, Labros Fragkedis, and Panayiotis Koutsabasis

  24. [24]

    Nels Numan, Gabriel Brostow, Suhyun Park, Simon Julier, Anthony Steed, and Jessica Van Brummelen. 2025. CoCreatAR: Enhancing Authoring of Outdoor Aug- mented Reality Experiences Through Asymmetric Collaboration. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems(Yokohama Japan, 2025-04-26). ACM, 1–22. doi:10.1145/3706598.3714274

    arXiv 2025

  25. [25]

    P. O’Neill. 2016.The Culture of Curating and the Curating of Culture(s). MIT Press. https://books.google.com/books?id=EFT5DwAAQBAJ

    2016

  26. [26]

    Young, Aljosa Smolic, Siobhán Dunne, and Helen Shenton

    Néill O’dwyer, Emin Zerman, Gareth W. Young, Aljosa Smolic, Siobhán Dunne, and Helen Shenton. 2021. Volumetric Video in Augmented Reality Applications for Museological Narratives: A User Study for the Long Room in the Library of Trinity College Dublin. 14, 2 (2021), 22:1–22:20. doi:10.1145/3425400

    work page doi:10.1145/3425400 2021

  27. [27]

    Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction

    Daniele Pannone, Alessia Castronovo, Maurizio Mancini, Gian Luca Foresti, Claudio Piciarelli, Rossana Gabrieli, Muhammad Yasir Bilal, and Danilo Avola. 2025.Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction. arXiv:2507.13719 [cs] doi:10.48550/arXiv.2507.13719

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2507.13719 2025

  28. [28]

    Daniela Petrelli. 2019. Making virtual reconstructions part of the visit: An exploratory study.Digital Applications in Archaeology and Cultural Heritage15 (09 2019), e00123. doi:10.1016/j.daach.2019.e00123

    work page doi:10.1016/j.daach.2019.e00123 2019

  29. [29]

    PICO Developer Center. 2026. PICO XR Check (PXRC): Functional Requirement Checklist for Official Apps. https://developer.picoxr.com/document/distribute/ functional-requirement-checklist-for-official-apps/ Accessed 2026-03-28

    2026

  30. [30]

    Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, and Phillip H Jones. 2019. Comparing energy efficiency of CPU, GPU and FPGA implementations for vision kernels. In2019 IEEE international conference on embedded software and systems (ICESS). IEEE, 1–8

    2019

  31. [31]

    Marko Radanovic, Kourosh Khoshelham, and Clive Fraser. 2023. Aligning the real and the virtual world: Mixed reality localisation using learning-based 3D–3D model registration.Advanced Engineering Informatics56 (2023), 101960

    2023

  32. [32]

    Somaiieh Rokhsaritalemi, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. 2020. A Review on Mixed Reality: Current Trends, Challenges and Prospects. 10, 2 (2020), 636. doi:10.3390/app10020636

    work page doi:10.3390/app10020636 2020

  33. [33]

    Filippo Sanfilippo, Marius Tataru, Minh Tuan Hua, Inge Johan Straumsøy Johans- son, and Diana Andone. 2025. Gamifying Cultural Immersion: Virtual Reality and Mixed Reality in City Heritage. 17, 4 (2025), 893–911. doi:10.1109/TG.2025. 3553712

    work page doi:10.1109/tg.2025 2025

  34. [34]

    Thomas Schubert, Frank Friedmann, and Holger Regenbrecht. 2001. The expe- rience of presence: Factor analytic insights.Presence: Teleoperators & Virtual Environments10, 3 (2001), 266–281

    2001

  35. [35]

    Michael James Scott, Alcwyn Parker, Edward J Powley, Rob Saunders, Jenny R Lee, Phoebe Herring, Douglas Brown, and Tanya Krzywinska. 2018. Towards an inter- action blueprint for mixed reality experiences in GLAM spaces: The augmented telegrapher at Porthcurno Museum. InProceedings of the 32nd International BCS Human Computer Interaction Conference. BCS Le...

    2018

  36. [36]

    Xingdong Sheng, Shijie Mao, Yichao Yan, and Xiaokang Yang. 2024. Review on SLAM algorithms for Augmented Reality.Displays84 (2024), 102806

    2024

  37. [37]

    Hall, and Michael Nebeling

    Maximilian Speicher, Brian D. Hall, and Michael Nebeling. 2019. What Is Mixed Reality?. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow Scotland Uk, 2019-05-02). ACM, 1–15. doi:10.1145/3290605. 3300767

    doi:10.1145/3290605 2019

  38. [38]

    Flanders Fields

    Birger Stichelbaut, Gertjan Plets, and Keir Reeves. 2021. Towards an Inclusive Curation of WWI Heritage: Integrating Historical Aerial Photographs, Digital Museum Applications and Landscape Markers in “Flanders Fields” (Belgium). 11, 4 (2021), 344–360. doi:10.1108/JCHMSD-04-2020-0056

    work page doi:10.1108/jchmsd-04-2020-0056 2021

  39. [39]

    Tanh Quang Tran, Tobias Langlotz, and Holger Regenbrecht. 2024. A Survey On Measuring Presence in Mixed Reality. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems(New York, NY, USA, 2024-05-11)(CHI ’24). Association for Computing Machinery, 1–38. doi:10.1145/3613904.3642383

    arXiv 2024

  40. [40]

    Mariapina Trunfio, Timothy Jung, and Salvatore Campana. 2022. Mixed Reality Experiences in Museums: Exploring the Impact of Functional Elements of the Devices on Visitors’ Immersive Experiences and Post-Experience Behaviours. 59, 8 (2022), 103698. doi:10.1016/j.im.2022.103698

    arXiv 2022

  41. [41]

    2026.Unity 6 User Manual

    Unity Technologies. 2026.Unity 6 User Manual. https://docs.unity3d.com/6000. 0/Documentation/Manual/UnityManual.html

    2026

  42. [42]

    Fernández-Caramés

    Aida Vidal-Balea, Paula Fraga-Lamas, and Tiago M. Fernández-Caramés. 2024. Advancing NASA-TLX: Automatic User Interaction Analysis for Workload Evalu- ation in XR Scenarios. In2024 IEEE Gaming, Entertainment, and Media Conference (GEM)(Turin, Italy, 2024-06-05). IEEE, 1–6. doi:10.1109/GEM61861.2024.10585425

    arXiv 2024

  43. [43]

    Claas Willem Visser, Philipp Erhard Frommhold, Sander Wildeman, Robert Met- tin, Detlef Lohse, and Chao Sun. 2015. Dynamics of high-speed micro-drop impact: numerical simulations and experiments at frame-to-frame times below 100 ns.Soft matter11, 9 (2015), 1708–1722

    2015

  44. [44]

    Jialin Wang, Rongkai Shi, Wenxuan Zheng, Weijie Xie, Dominic Kao, and Hai- Ning Liang. 2023. Effect of frame rate on user experience, performance, and simulator sickness in virtual reality.IEEE Transactions on Visualization and Computer Graphics29, 5 (2023), 2478–2488

    2023

  45. [45]

    Dana Linnell Wanzer, Kelsey Procter Finley, Steven Zarian, and Noreen Cortez

  46. [46]

    Jiayi Xu, Chenchen Shan, and Yixuan Lv. 2025. Reconstruction of Visual Appeal and Renewal of Dynamic Environments: Computational Methods and Innovative Strategies in the Future-oriented Digital Transformation of Heritage Museums. 127a (2025). doi:10.61091/jcmcc127a-356

    work page doi:10.61091/jcmcc127a-356 2025

  47. [47]

    Bohuan Xue, Xiaoyang Yan, Jin Wu, Jintao Cheng, Jianhao Jiao, Haoxuan Jiang, Rui Fan, Ming Liu, and Chengxi Zhang. 2024. Visual-Marker-Based Localization for Flat-Variation Scene. 73 (2024), 1–16. doi:10.1109/TIM.2024.3372231

    arXiv 2024

  48. [48]

    Inhwa Yeom and Woontack Woo. 2021. Digital Twin as A Mixed Reality Platform for Art Exhibition Curation. In2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)(Lisbon, Portugal, 2021-03). IEEE, 424–425. doi:10.1109/VRW52623.2021.00094

    arXiv 2021

  49. [49]

    Ji Hyun Yi and Hae Sun Kim. 2021. User Experience Research, Experience Design, and Evaluation Methods for Museum Mixed Reality Experience. 14, 4 (2021), 48:1–48:28. doi:10.1145/3462645

    work page doi:10.1145/3462645 2021

  50. [2008]

    InProceedings of the 3rd International Conference on Digital Interactive Media in Entertainment and Arts(Athens Greece, 2008-09-10)

    Bridging the Gap between the Digital and the Physical: Design and Evalua- tion of a Mobile Augmented Reality Guide for the Museum Visit. InProceedings of the 3rd International Conference on Digital Interactive Media in Entertainment and Arts(Athens Greece, 2008-09-10). ACM, 120–127. doi:10.1145/1413634.1413660

    arXiv 2008

  51. [2020]

    Experiencing flow while viewing art: Development of the Aesthetic Ex- perience Questionnaire.Psychology of Aesthetics, Creativity, and the Arts14, 1 Toward Site-Aware MR Art Exhibitions Conference acronym ’XX, June 03–05, 2018, Woodstock, NY (2020), 113

    2018

  52. [2022]

    doi:10.1145/3522743

    Design and User Experience of a Hybrid Mixed Reality Installation that Promotes Tinian Marble Crafts Heritage.Journal on Computing and Cultural Heritage15 (07 2022). doi:10.1145/3522743

    work page doi:10.1145/3522743 2022