Evaluating 3D Visual Comparison Techniques for Change Detection in Virtual Reality
Bibliographic record
Abstract
Change detection (CD) is critical in everyday tasks. While current algorithmic approaches for CD are improving, they remain imprecise, often requiring human intervention. Cognitive science research focuses on understanding CD mechanisms, especially through change blindness studies. However, these do not address the primary requirement in real-life CD - detecting changes as effectively as possible. Such a requirement is directly relevant to the visual comparison field - studying visualisation techniques to compare data and identify differences or changes effectively. Recent studies have used Virtual Reality (VR) to improve visual comparison by providing an immersive platform where users can interact with 3D data at a real-life scale, enhancing spatial reasoning. We believe VR could also improve CD performance accordingly. Particularly, VR offers stereoscopic depth perception over traditional displays, potentially enhancing the detection of spatial change. In this paper, we develop and analyse three 3D visual comparison techniques for CD in VR: Sliding Window, 3D Slider, and Switch Back. These techniques are evaluated under synthetic but realistic environments and frequently occurring Perceptual Challenges, including different Changed Object Size, Lighting Variation, and Scene Drift conditions. Experimental results reveal significant differences between the techniques in detection time measures and subjective user experience.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".