Towards more accurate immersive 3D sketching
Bibliographic record
Abstract
This doctoral work aims to reduce the gap in knowledge of how users utilize immersive 3D sketching through a better understanding of what affects them while drawing in virtual reality.My first goal is to know more about the reasons behind the reduced accuracy of 3D sketches compared to 2D ones, with an eye towards potential differences between users with different skill levels.While previous research described the various challenges of immersive 3D drawing, those descriptions have focused mostly on identifying the reasons regarding why people draw worse in 3D than 2D.In this doctoral work, my goal is to understand how the perceptual and cognitive limitations of humans affect their behaviours when working in virtual environments.The second goal of this doctoral work is to develop new user interfaces that help novice users draw better using virtual reality.I aim to allow users to express their ideas more easily, through improving stroke quality and global shape likeness, without affecting their stroke expressiveness.The user's stroke quality measures (locally) how close a drawn stroke is to an intended one and shape likeness measures how (globally) similar a drawn object is to the intended shape.Improving both of these qualities makes sketching a useful tool to share concepts and to aid the user's memory.My work on these two goals resulted in four different projects.Each project was previously published, and I present the full text of those four studies in this cumulative format dissertation.The four projects include 1) a study of the effect of changing the viewpoint when drawing in 3D, 2) a study of the effect of the depth perception problems of stereo displays on hand pointing in peripersonal space, 3) a system called Multiplanes, and 4) a system called Smart3DGuides.In addition to these projects, I posed a critical reflection on the user interface requirements for immersive 3D drawing systems to inform the design of future interfaces.Finally, I address this dissertation to user interface designers and HCI and design researchers who are interested in using virtual reality as a new medium to sketch.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".