Seamless and Efficient Interactions within a Mixed-Dimensional Information Space

Chen Chen

REVIEW 4 major objections 5 minor 285 references

Seamless and Efficient Interactions within a Mixed-Dimensional Information Space

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The author sets out to prove that a mixed-dimensional information space combining 2D and 3D content can be navigated seamlessly and efficiently when three specific kinds of seams are each addressed by a dedicated strategy.

desk verdict An honest dissertation that reprints three solid systems; the synthetic framing is useful but the thesis statement reaches beyond the evidence. read the letter →

arxiv 2506.04545 v1 pith:VX2XW3F4 submitted 2025-06-05 cs.HC

Chen Chen This is my paper

classification cs.HC

keywords mixed-dimensionalinformationspacehuman-computerinteractionmixedreality3DdesignfeedbackgenerativeAIinstructiondocumentsvirtualmedicalcontouring

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This dissertation introduces the concept of a mixed-dimensional information space, where users work with both 2D entities such as text and images and 3D entities such as models, physical scenes, and medical volumes, and must often move between them. The author's thesis is that the seams and inefficiencies in such a space can be removed with three strategies: strategically integrating multimodal intelligence to fix perspective mismatching, using environmental and behavioral context cues to fix spatial misalignment, and combining 2D and 3D representations to fix dimensional incongruence. Three systems carry the argument: MemoVis, a generative-AI text-editor interface for creating reference images for 3D design feedback; PaperToPlace, which turns paper instructions into spatialized, context-aware MR guidance; and VRContour, which brings medical contouring into virtual reality. A careful reader would care because the three strategies give interaction designers a concrete route toward interfaces that keep users focused on their primary task rather than on the mechanics of switching between dimensions.

What carries the argument

The central object is the mixed-dimensional information space itself, defined as an information space containing entities represented in both 2D and 3D, with three named failure modes: perspective mismatching, spatial misalignment, and dimensional incongruence. The load-bearing machinery is the mapping from each failure mode to a technical strategy: multimodal intelligence, realized as vision-language viewpoint suggestion and condition-guided image generation, for perspective mismatch; context-aware placement, realized as importance maps, occlusion maps, and simulated-annealing optimization, for spatial alignment; and blended 2D/3D visualization with brush-based contouring for dimensional congruence. Each system is constructed so that one of these three mechanisms carries the seam-removal work.

What would settle it

A controlled comparison in a mixed-dimensional task outside the three applications, such as navigating a building with a 2D map overlaid in augmented reality or reviewing architectural models on a tablet, that shows no reduction in context switching or task time when these strategies are applied would undercut the thesis's generalization.

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Extended reading notes

Core claim

The core claim is that a seamless and efficient interaction experience within a mixed-dimensional information space is attainable by directly attacking the three seams that arise when 2D and 3D entities meet. Perspective mismatching is addressed by MemoVis, which uses a vision-language model to suggest camera viewpoints anchored to the text a user is typing, and three image modifiers built on conditional diffusion models to generate in-context reference images for design feedback. Spatial misalignment is addressed by PaperToPlace, which parses paper instructions into steps, associates those steps with real-world objects, and uses an optimization over importance and occlusion maps to place each virtual step where it is readable without blocking hands or view. Dimensional incongruence is addressed by VRContour, which combines 3D volumetric rendering with 2D slice contouring so that users can see anatomy in depth while still outlining on familiar 2D planes. Together the three systems are offered as evidence that the three strategies can generalize across the mixed-dimensional information space.

Load-bearing premise

The load-bearing premise is that the three chosen applications, 3D design feedback, procedural instruction, and medical contouring, are representative of the whole mixed-dimensional information space, so the three strategies carry over to other 2D/3D contexts without a broader comparative study.

Editorial extensions

If this is right

If the thesis is right, interaction designers have a transferable playbook: fix perspective mismatch with multimodal intelligence, fix spatial mismatch with context cues, and fix dimensional mismatch with blended 2D/3D representations.
Non-experts can participate in 3D design review: MemoVis's viewpoint suggestions and image modifiers let feedback providers create in-context reference images without 3D or image-editing skills, and the reported study shows significantly reduced camera-navigation time.
Existing paper instructions can be upgraded to MR without being rewritten from scratch; PaperToPlace's authoring pipeline segments documents, and its consumption pipeline places steps optimally to reduce context-switching effort.
Domain-specific workflows such as medical contouring can be moved into VR if the interface preserves the familiar 2D slice-based task inside a 3D volumetric scene.
The dissertation's evaluation pattern, measuring navigation time, context switching, and image explicitness, provides a template for assessing whether any mixed-dimensional interface is actually seamless.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the three challenges could be read as a design checklist for any interface that mixes 2D and 3D content, so future systems could be evaluated for perspective, placement, and dimensional seams before implementation.
Editorial inference: the viewpoint-suggestion method, which matches a text comment against pre-rendered views using a vision-language model, likely transfers to other text-anchored 3D navigation tasks such as finding a view of a model, scene, or volume from a natural-language description.
Editorial inference: PaperToPlace's placement optimization could be made continuously adaptive, repositioning instruction steps as gaze and hand tracking indicate a new area of focus, an extension the dissertation only partially explores.
Editorial inference: the dissertation's three applications are a convenience sample; testing the three strategies on a fourth task, such as collaborative data analysis or education, would show whether the claimed generalization holds.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First, know this: it is a dissertation that reprints three peer-reviewed papers (MemoVis, PaperToPlace, VRContour), clearly disclosed in the acknowledgements. The genuinely new material is the opening framing — 'mixed-dimensional information space' and the three challenges/seams — not any new experimental result.

What it does well: the three systems are real contributions with credible evaluations. MemoVis has a within-subjects study (n=14) with a significant navigation-time reduction and a second evaluator study; PaperToPlace has two studies (n=12 each) on authoring and consumption; VRContour draws on domain-specific expert feedback. The framing is useful: perspective mismatching, spatial misalignment, and dimensional incongruence name real seams, and mapping each to a design strategy gives designers something to take away. The author also handles the reprint structure honestly, and each chapter ends with a limitations section that admits the small samples and controlled settings. That is more transparent than many compilations.

The soft spots are in the scope of the claims. The thesis statement says seamless and efficient interaction 'can be achieved by' strategies (1)–(3), but no chapter isolates a strategy. Each system bundles the named strategy with several other design decisions — MemoVis couples viewpoint suggestion with three GenAI modifiers and segmentation; PaperToPlace bundles document parsing, key-object suggestion, placement optimization, and hands-free menus; VRContour combines volumetric rendering, brush tools, and 2D/3D views. So the evidence shows three integrated systems are usable and preferred, not that the three named strategies are the operative causes. The stress-test note lands. The reader's separate concern about generalizing beyond the three applications is also fair: the sample sizes are tiny by quantitative standards, much of the evidence is qualitative, and no code or data is released.

That said, these flaws are proportionate to the genre. It is a dissertation, and the compilation plus framing is a reasonable way to present a body of work. The 'conditional' verdict is right: the synthesis is promising, but it would need a comparative or ablation study to support the causal wording.

Who is this for: HCI/XR researchers interested in 2D/3D interaction design, and PhD students looking at how to frame a system dissertation. It deserves a serious referee if the question is whether the synthesis is worth publishing as a position/overview piece; it should not be treated as a new empirical contribution. I would cite the original papers, not the dissertation, but I would bring the framing into a discussion of mixed-dimensional interaction.

Referee Report

4 major / 5 minor

Summary. This dissertation proposes the concept of a mixed-dimensional information space, in which users interact with entities represented in both 2D and 3D, and claims that seamless and efficient interaction in such a space can be achieved through three strategies: (1) integrating multimodal intelligence to address perspective mismatching, (2) using environmental and behavioral context cues to address spatial misalignment, and (3) combining 2D and 3D representations to address dimensional incongruence. The body of the dissertation consists of three previously published systems: MemoVis, a GenAI-assisted tool for creating reference images for 3D design feedback; PaperToPlace, a workflow that converts paper instructions into spatially optimized mixed-reality guidance; and VRContour, a virtual-reality system for medical contour delineation. Each system is evaluated with at least one user study, including formative studies, quantitative measurements, system usability scales, and qualitative codebooks. The dissertation concludes with a general discussion of context awareness, cognitive load, and future work.

Significance. If the thesis-level claim is taken at face value, the dissertation offers a transferable design recipe for HCI researchers building mixed-dimensional interfaces: use multimodal intelligence for perspective matching, context cues for spatial placement, and mixed 2D/3D representations for dimensionally incongruent tasks. The main strengths are that all three systems are fully implemented and grounded in real user needs, each is backed by at least one empirical study with mixed quantitative and qualitative evidence, and the reprint status of the three chapters is explicitly disclosed. The use of pre-trained CLIP, ControlNet, SAM, and a fine-tuned language model, rather than models fitted to the evaluation outcomes, reduces circularity risk. The evaluations include both producer-side and consumer-side assessments, which is particularly valuable for MemoVis. However, the connection between the evidence and the broad thesis statement is weaker than the abstract and Section 1.1 suggest: the studies are small, mostly qualitative, and each strategy is evaluated only as part of an integrated system, so the causal attribution of the named strategies is not isolated by the data.

major comments (4)

[§1.1, Thesis Statement] The claim that seamless and efficient interaction in a mixed-dimensional information space 'can be achieved by' the three named strategies is underdetermined by the evidence. In MemoVis (§3.4), the real-time viewpoint suggestion is bundled with the three GenAI image modifiers and a text editor; the navigation-time comparison in §3.5.1 (F1,24=7.398, p=.018) supports the whole system, not the specific strategy of 'multimodal intelligence.' The same bundling occurs in PaperToPlace (§4.6), where the placement optimizer is combined with document parsing, gesture menus, and hands-free interaction, and in VRContour (§5.4), where volumetric rendering and brush tools are introduced together. Because the thesis statement is the central claim of the dissertation, this mismatch should be resolved either by narrowing the claim to the three demonstrated applications and strategies, or by adding component-level analyses that isolate each strategy's contribution.
[§3.5.1, Measures and Data Analysis] The reported test statistic F1,24 for navigation time is difficult to reconcile with the within-subjects study design. With 14 participants, or 13 after excluding PF14, the error degrees of freedom should reflect participant-level clustering; F1,24 implies that individual comments or task-level observations were treated as independent units. Non-independent observations as the unit of analysis overstate the statistical significance of the result. Please report a mixed-effects model with participant as a random effect, or aggregate the navigation time per participant before performing the ANOVA. This issue is load-bearing because this quantitative effect is the primary evidence for the 'efficiency' component of Strategy (1).
[§1.3 and §6.4] The dissertation generalizes from three task domains, namely 3D design feedback, procedural instructions, and medical contouring, to the entire 'mixed-dimensional information space,' but no argument establishes that these three applications span the space's important interaction challenges. The limitations sections in §3.7, §4.9, and §5.7.3 appropriately acknowledge small samples, narrow task scopes, and the need for broader deployment, and §6.4 also outlines limitations; however, these qualifications are not carried back to the thesis statement in §1.1 or to the abstract. The authors should either explicitly restrict the thesis to the three studied application contexts or provide a coverage argument in Chapter 6 that justifies broader applicability. Without this, the central claim exceeds the evidence, even though each individual system is credible.
[§5.4 and §5.6] The VRContour evaluation conflates 'combining 2D and 3D representations' with the overall VR experience. The system simultaneously introduces volumetric rendering, new brush-based interaction techniques, and a 2D/3D mixed working set (§5.4), so any significant difference reported in §5.6 could plausibly be attributed to immersion, input modality, or task novelty rather than to dimensional congruence specifically. An evaluation that includes a 3D-only condition, a 2D-slice-only condition, or at least a systematic analysis of which specific feature drove the observed benefits would be needed to isolate the third thesis strategy.

minor comments (5)

[Abstract and §1.1] There is a grammatical error in the thesis statement: 'An seamless' should be 'A seamless.' The same error appears in the abstract.
[Appendix D] The heading 'Glossory of Terms' is a typo; it should read 'Glossary of Terms.'
[Chapter 5] In the table of contents and the chapter text, several instances of 'V olumetric' and 'V olumeric' appear with a stray space; these should be corrected to 'Volumetric.'
[§2.2] The phrase 'Prior research research' is a duplicated word and should read 'Prior research.'
[Chapters 3–5] Each chapter is explicitly identified as a reprint of a published paper, which is good practice, but the dissertation would benefit from a brief introductory note at the start of each chapter explaining how the chapter relates to the overarching thesis and whether any content was added or modified for the dissertation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the thesis strategies are supported by external baselines and pre-trained models rather than by construction.

full rationale

The dissertation's three systems are each evaluated against external baselines or existing workflows, and the thesis-level claim in Section 1.1 is a design generalization rather than a derivation that reduces to its inputs. MemoVis's viewpoint suggestion relies on a pre-trained CLIP model and its image modifiers on pre-trained ControlNet, SAM, and inpainting models, none of which are fitted to the study outcomes; the navigation-time reduction is measured empirically against a search/sketch baseline. PaperToPlace's consumption pipeline is a computational placement optimization evaluated in within-subjects user studies, and its ML-assisted authoring pipeline is assessed against a rule-based baseline and manual labeling, not against the same data used to fit the model. VRContour is a within-subjects comparison of VR contouring against a standard screen-based workflow, so its outcome measure is not defined in terms of the 2D/3D feature it claims to test. The self-citations present in the manuscript are explicit disclosures that the chapters reprint previously published papers; these citations are not invoked as load-bearing evidence for the dissertation's thesis, and no equation or fitted parameter is renamed as a prediction. The strongest available criticism, that the thesis's causal attribution is underdetermined because each strategy is evaluated only as part of a full system, is a concern about external validity or experimental isolation, not circularity under the definitions used in this analysis.

Assumptions & free parameters 3 free parameters · 3 assumptions · 3 invented entities

The dissertation's central claim relies on the methodological premise that small qualitative studies can validate design guidelines, the semantic assumption that CLIP similarities map to task-relevant viewpoints, and the domain assumption that reducing context-switching improves task efficiency. The framing concepts ('mixed-dimensional information space', 'seams') are interpretive constructs rather than independently falsifiable entities.

free parameters (3)

ControlNet conditioning strengths = 0.7 (scribble), 0.3 (depth)
Empirically set in MemoVis to emphasize newly added objects when composing scribble and depth conditions (Section 3.4.2, Modifier 1).
Viewpoint sampling grid = 5 bins per axis, 30-degree intervals, r in {0.5, 1.0, 1.5}
Discretization chosen by the authors to build the CLIP viewpoint database for MemoVis (Section 3.4.1).
Placement optimizer cost weights = not specified in visible text
PaperToPlace consumption pipeline optimizes instruction placement with constraint and cost terms; the weights are not visible in the truncated chapter (Section 4.6.4).

assumptions (3)

domain assumption User-centered design and small-n user studies provide valid evidence for design guidelines
The dissertation uses case studies and qualitative analysis to support the thesis; this is a methodological premise of HCI research (Chapter 1).
domain assumption CLIP cosine similarity between text and rendered viewpoints identifies task-relevant camera views
MemoVis's viewpoint suggestion relies on this, Section 3.4.1.
domain assumption Reducing context switches and occlusions improves task efficiency in MR procedures
PaperToPlace's design and evaluation assume this, Section 4.1 and 4.3.

invented entities (3)

Mixed-dimensional information space
purpose: Framing construct to unify interactions involving 2D and 3D content
Introduced in Chapter 1; used to organize the three systems but not directly measured or falsified.
Seams
purpose: Metaphor for boundaries between 2D and 3D entities that cause interaction costs
Defined in Chapter 1; illustrated with examples but not operationalized as a measurable quantity.
Perspective mismatching, spatial misalignment, dimensional incongruence
purpose: Three challenge categories the thesis claims to address
Grounded in formative studies (interviews, feedback analysis) but presented as interpretive categories rather than independently falsifiable predictions.

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Cite this review

Pith. "Pith review of Seamless and Efficient Interactions within a Mixed-Dimensional Information Space." pith.science (2026). https://pith.science/paper/VX2XW3F4

@misc{pith2026250604545,
  author       = {Pith},
  title        = {Pith review of: Seamless and Efficient Interactions within a Mixed-Dimensional Information Space},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VX2XW3F4}},
  note         = {Machine review of arXiv:2506.04545}
}

read the original abstract

Mediated by today's visual displays, information space allows users to discover, access and interact with a wide range of digital and physical information. The information presented in this space may be digital, physical or a blend of both, and appear across different dimensions - such as texts, images, 3D content and physical objects embedded within real-world environment. Navigating within the information space often involves interacting with mixed-dimensional entities, visually represented in both 2D and 3D. At times, interactions also involve transitioning among entities represented in different dimensions. We introduce the concept of mixed-dimensional information space, encompassing entities represented in both 2D and 3D. Interactions within the mixed-dimensional information space should be seamless and efficient: users should be able to focus on their primary tasks without being distracted by interactions with or transitions between entities. While incorporating 3D representations into the mixed-dimensional information space offers intuitive and immersive ways to interact with complex information, it is important to address potential seams and inefficiencies that arise while interacting with both 2D and 3D entities. This dissertation introduces new interactive techniques and systems to realize seamless and efficient interactions within the mixed-dimensional information space. This dissertation introduces three interactive systems: MemoVis which aims to use emergent generative AI to help users create reference images for 3D design feedback; PaperToPlace which demonstrates how paper-based instruction documents can be transformed and spatialized into a context-aware MR experience; and VRContour which explores how contour delineation workflow can be brought into VR.

Figures

Figures reproduced from arXiv: 2506.04545 by the authors.

**Figure 1.1.** Illustrative figures showing the challenges of interacting within mixed-dimensional information space. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 [PITH_FULL_IMAGE:figures/full_fig_p010_1_1.png] view at source ↗

**Figure 1.2.** Examples of user experience evaluations of the recent Apple Vision Pro, as reported by the Wall Street Journal [275], include: (a) virtually rendered widgets obstructing real-world cooking activities, and (b) the need to manually adjust and reposition these widgets in response to changes in real-world activities. design feedback. • Spatial misalignment. The spatial placement of 2D content may not always be optimal i… view at source ↗

Figures from the paper (61 more)

**Figure 2.1.** Figure 2.1: Demonstrative figure of Reality-Virtually (RV) continuum - a spectrum describing the design of Mixed Reality (MR) display that ranges from a completely real environment to a complete virtual environment [194]. Demonstrative figure is captured from [111]. platform for…

**Figure 3.1.** Figure 3.1: MemoVis is a browser-based text editor interface that assists feedback providers to create companion reference images for 3D design. Feedback providers can (a) explore the 3D model in a 3D content viewer and (b) type the textual feedback comments in a rich-text edito…

**Figure 3.** Figure 3: c), and [PITH_FULL_IMAGE:figures/full_fig_p054_3.png]

**Figure 3.2.** Figure 3.2: Example reference images from Polycount [37, 38]. (a) Initial design of discussion thread 6 and (b) feedback from OFP6-6; (c) initial design of discussion thread 8 and feedback from OFP8-1 (f) and OFP8-2 (d - e); (g) initial design of discussion thread 2; (h) initial…

**Figure 3.3.** Figure 3.3: Examples of the suggested viewpoints based on the typed feedback comments (leftmost column). We show viewpoints with top-4 highest CLIP similarity scores for an office 3D model (a - e), a car model (f - j), and a samurai boy model (k - o). a, f, and k show the bird-e…

**Figure 3.** Figure 3: shows examples of suggested viewpoints of the pegboard in an office (Figure 3.3a [PITH_FULL_IMAGE:figures/full_fig_p059_3.png]

**Figure 3.4.** Figure 3.4: Examples of creating reference image using the text + scribble modifier. (a) Initial design; (b) associated depth map; (c) manually drawn scribbles (black strokes) with the white strokes indicating the removed geometries; (d) the depth map with the scribbling area be…

**Figure 3.** Figure 3: a with the depth map shown in Figure 3.4b, the feedback provider only has to scribble [PITH_FULL_IMAGE:figures/full_fig_p061_3.png]

**Figure 3.** Figure 3: j. To address this, MemoVis detects the mesh primitives to be removed from the image [PITH_FULL_IMAGE:figures/full_fig_p062_3.png]

**Figure 3.5.** Figure 3.5: Examples showing how the feedback providers can stage the 3D model into different scene with the grab’n go modifier. (a) The initial 3D design of a car; (b, d) synthesized image generated by scribble + text modifier with ControlNet conditioned on depth. The prompt “a…

**Figure 3.6.** Figure 3.6: Examples of continuous composing. (a) Reference image of [PITH_FULL_IMAGE:figures/full_fig_p063_3_6.png]

**Figure 3.7.** Figure 3.7: Examples of visualizing feedback that is related to adding new additional object with text + paint modifier. (b - c) shows a wall clock is successfully added to the view. (d) aims to add a curved computer display which is more complicated in terms of shapes, geometri…

**Figure 3.** Figure 3: shows the design of MemoVis. With the text [PITH_FULL_IMAGE:figures/full_fig_p064_3.png]

**Figure 3.8.** Figure 3.8: Text + paint modifier can be used to fix the glitches of the images created by text + scribble and grab’n go modifiers. the right mouse button. The left and right mouse button indicate keeping and removing the key object(s) inside the enclosed box of the synthesized …

**Figure 3.9.** Figure 3.9: Survey responses and navigating time measurements from Study 1. (a) Participants’ responses of Q1 - Q4; (b) the total navigating time of baseline and MemoVis. PF14 was excluded from [PITH_FULL_IMAGE:figures/full_fig_p069_3_9.png]

**Figure 3.** Figure 3: a shows that compared to the baseline, more [PITH_FULL_IMAGE:figures/full_fig_p070_3.png]

**Figure 3.10.** Figure 3.10: Examples of inspirations and creativity support. (a, c, e) show the selected viewpoints of the initial design; (b, d, f) show the created visual references by PF4. The reported unexpected components are highlighted by red circle. MemoVis for inspiring new ideas, whi…

**Figure 3.** Figure 3: demonstrates three examples with unexpected creativity that were appreciated by [PITH_FULL_IMAGE:figures/full_fig_p072_3.png]

**Figure 3.11.** Figure 3.11: Examples of unsatisfactory reference images. (a) initial sketch from PF4 to describe an indoor plant. (b) MemoVis failed to generate the desirable image. (c) with a different camera view, PF4 successfully created the expected reference image. (d) initial sketch from…

**Figure 3.12.** Figure 3.12: Survey responses of Study 2. (a) Participants’ response of each 3D design feedback, in a scale of 1 to 5 where 1 indicates strong disagree and 5 indicates strongly agree; (b) cumulative analysis of the % of 3D design feedback for each Likert scale by two interface c…

**Figure 3.** Figure 3: a shows the Likert rating for each feedback. We found that around [PITH_FULL_IMAGE:figures/full_fig_p076_3.png]

**Figure 3.13.** Figure 3.13: Examples of 3D design feedback created by the baseline interfaces. The red traces indicate the annotations drawn by the feedback provider participant PF2. an example of the reference image created by PF10, where PD2 judged: “the structure between the bed and the flo…

**Figure 3.14.** Figure 3.14: Examples of 3D design feedback created by MemoVis. The participants strongly agreed, neither agree nor disagree, disagreed and strongly disagreed that the reference image from the 3D design feedback (a), (b), (c) and (d) can convey the gist of the textual comment, r…

**Figure 3.** Figure 3: c, PD4 [PITH_FULL_IMAGE:figures/full_fig_p078_3.png]

**Figure 4.1.** Figure 4.1: Overview of PaperToPlace: (a) The author creates an MR experience by taking a snapshot of a paper document, with an optional ML-supported pipeline for associating key objects with each instruction step; (b) The consumer can browse the spatialized instruction steps us…

**Figure 4.** Figure 4: c shows an example of a spatialized instruction step, where the step “ [PITH_FULL_IMAGE:figures/full_fig_p091_4.png]

**Figure 4.2.** Figure 4.2: Preliminary needs-finding tasks. (a) Making a cup of coffee using a coffee machine (PP1). (b) Making a chocolate cake in a mug with a microwave (PP3). time for an unpaid study. We also used the existing instructions [18, 29] created by professional writers to minimiz…

**Figure 4.3.** Figure 4.3: Preliminary needs-finding study results. (a) Example context switching while PP2 was attempting to map instructions with real-world objects. (b) The annotated timestamps showing participants’ current focus as either the document or real-world activities. (c) PP1’s de…

**Figure 4.4.** Figure 4.4: PaperToPlace system overview. We assume a spatial profile (h, red block) was pre-created. The author uses the authoring pipeline (a - g, blue blocks) to extract the document profile for the MR experience. With consuming pipeline (i, green block), the instruction step…

**Figure 4.5.** Figure 4.5: (a) Creation of an anchoring surface, visualized as a semi-transparent mask, using touch controllers. (b) Examples of anchoring surfaces in our experimental kitchen. Both scenes were captured as first-person MR views. 72 [PITH_FULL_IMAGE:figures/full_fig_p099_4_5.png]

**Figure 4.6.** Figure 4.6: Confusion matrices of the fine-tuned BERT model, with the ground truth generated by rule-based method (a) and manually labeling (b). difficult and time consuming. Instead of preparing such dataset for all instruction documents, we chose to focus on domain-specific da…

**Figure 4.** Figure 4: i, and Figure C.6 in Appendix C.2.2) [PITH_FULL_IMAGE:figures/full_fig_p104_4.png]

**Figure 4.** Figure 4: c that advocate the idea of delivering the right level of information only at the right time. [PITH_FULL_IMAGE:figures/full_fig_p105_4.png]

**Figure 4.7.** Figure 4.7: First-person view of rotating instruction step for (a) horizontal anchoring surface (e.g., countertop) and (b) vertical anchoring surface (e.g., microwave). horizontally (e.g., countertop) and vertically placed anchoring surfaces (e.g., the front surface of fridge) …

**Figure 4.8.** Figure 4.8: Examples of the importance map. (a) Third-person view; (b) First-person view through MR; (c) First-person view of the key object (sink) containing four anchoring surfaces; (c) Importance map from single frame; (d) Overall importance map from a set of frames. The red …

**Figure 4.9.** Figure 4.9: Example of occlusion map. (a) An example placement of the instruction step anchored next to the sink; (b) Visualization of the generated occlusion map, see [PITH_FULL_IMAGE:figures/full_fig_p111_4_9.png]

**Figure 4.10.** Figure 4.10: (a) Example placement attempts (the green trace). The darker color of the anchoring surface indicates a lower Ctotal; (b - d) Example optimized cost over each iteration using greedy algorithm (b) and simulated annealing approach (c). To increase readability, log sca…

**Figure 4.11.** Figure 4.11: Results of authoring pipeline evaluations. (a - d) The overall SUS, weighted TLX scores, TCT of extracting document profiles, and the average task completion time for deciding each instruction step with segmented instruction step; (d) Survey results of how participa…

**Figure 4.** Figure 4: demonstrates an example optimization [PITH_FULL_IMAGE:figures/full_fig_p115_4.png]

**Figure 4.12.** Figure 4.12: Baseline scene. Third-person (a) and first-person view through MR (b). instruction step. Overall, 10 participants were satisfied with the MR experience they authored ( [PITH_FULL_IMAGE:figures/full_fig_p118_4_12.png]

**Figure 4.13.** Figure 4.13: Consumption pipeline evaluation results. (a - b) Overall SUS and TLX score; (c) Survey results of how participants considered the overall consumption experience of PaperToPlace faster and easier, versus baseline. “B/L” and “P2P” indicate baseline and PaperToPlace co…

**Figure 4.14.** Figure 4.14: Results of context switching evaluations. (a) The total number of context switching while using the monolithic document and PaperToPlace. The average time (b), dhead (d), and θhead (f) during each episode; The total time (c), lhead (e), and θhead (g) during all epis…

**Figure 4.** Figure 4: c), [PITH_FULL_IMAGE:figures/full_fig_p121_4.png]

**Figure 5.1.** Figure 5.1: (a) Workflow of today’s RT treatment [70]. (b - d) Contouring using Eclipse [6]; For delineating contours, a 2D brush is used (b); while clicking the mouse, an area is colored (c); upon releasing the mouse, the contour is defined by the colored area (d). (e) Three pl…

**Figure 5.** Figure 5: a [PITH_FULL_IMAGE:figures/full_fig_p129_5.png]

**Figure 5.2.** Figure 5.2: Insights and takeaways from preliminary interviews and iterative design. using VR for anatomy education, with an average of 18% of score improvement. Ma et al. [189] designed an AR applications by overlaying the CT dataset on top of real-world participants, and demon…

**Figure 5.3.** Figure 5.3: Design of VRContour. (a) Explorations of the design space by considering the dimension of information input and consumption. (b - j) Final prototype of four conditions generated by our preliminary autobiographical and iterative design process. of using this system sh…

**Figure 5.** Figure 5: b - d [PITH_FULL_IMAGE:figures/full_fig_p134_5.png]

**Figure 5.** Figure 5: shows key takeaways during each iteration. [PITH_FULL_IMAGE:figures/full_fig_p135_5.png]

**Figure 5.4.** Figure 5.4: Drawing/erasing contours using brush metaphors. (a - b) The contour is automatically generated after the VR pen is lifted from the tablet; (c - d) the new contour is re–generated upon the completion of erasing; (e - f) contours, visualized as a volume, could also be …

**Figure 5.5.** Figure 5.5: Contour on 2D. (a) Tracked tablet and VR pen; (b) real-world scene using VR pen and a tablet; (c) a blue frame indicating the physical boundary of the tablet; (d) the cutting planes could be zoomed in for gaining a detailed view; (e) oncologists can only draw inside …

**Figure 5.6.** Figure 5.6: Transforming 3D medical structure. (a) Two rays from the VR pen and tablet are initiated after translation start; (b) after the translation stop, the 3D medical structures are anchored to the new place; (c) during rotation mode, the quarternion of the near–hand ancho…

**Figure 5.7.** Figure 5.7: Contour tasks: (a - c) lungs from the thorax structure; (d - f) femoral heads from the abdomen-pelvic structure; (g - i) eye balls from the head–neck structure; (j - l) heart from the thorax structure. Red contours in c, f, i, and l indicate expert contours. Displaye…

**Figure 5.8.** Figure 5.8: Horizontal view of the study timeline. 117 [PITH_FULL_IMAGE:figures/full_fig_p144_5_8.png]

**Figure 5.9.** Figure 5.9: Quantitative results (∗ = p < .05, ∗∗ = p < .01, ∗ ∗ ∗ = p < .001) for DSC (a), time for initial explorations (b), and overall TCT (c) measured on four conditions; standard error is used to visualize the error bar and the overlaid yellow scatters indicate individual …

**Figure 5.10.** Figure 5.10: Experimental setups. (a – b) Participant could sit or stand while performing designated contouring tasks. (c) The base station mounted at the front of the room. (d) The base stations are mounted at the back of the room. (e) A panoramic view of the experimental offic…

**Figure 5.** Figure 5: shows quantitative results [PITH_FULL_IMAGE:figures/full_fig_p148_5.png]

**Figure 5.11.** Figure 5.11: Responses to post-study surveys. Purple arrow (top right) indicates the direction where a good system should be designed toward. were able to finish the instructed contour tasks within 10 min with C3. While it seems that the capability of direct contouring in 3D cou…

**Figure 5.** Figure 5: d - e approximate the average of % of attention that participants allocated [PITH_FULL_IMAGE:figures/full_fig_p149_5.png]

**Figure 6.1.** Figure 6.1: Examples of delivering 3D content using emergent display technologies: (a) spatial display by Looking Glass Factory [124]; (b) projector-based CAVE environment [103]; (c) 3D holographic fan. Designing for a broader range of tasks and user populations. We focus on des…

**Figure 3.** Figure 3: and Figure 3.14, copyrights of these images belong to the original content creators. [PITH_FULL_IMAGE:figures/full_fig_p163_3.png]

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