Toward More Comprehensive Evaluations of 3D Immersive Sketching, Drawing, and Painting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To understand current practice and explore the potential for more comprehensive evaluations of 3D immersive sketching, drawing, and painting, we present a survey of evaluation methodologies used in existing 3D sketching research, a breakdown and discussion of important phases (sub-tasks) in the 3D sketching process, and a framework that suggests how these factors can inform evaluation strategies in future 3D sketching research. Existing evaluations identified in the survey are organized and discussed within three high-level categories: 1) evaluating the 3D sketching activity, 2) evaluating 3D sketching tools, and 3) evaluating 3D sketching artifacts. The new framework suggests targeting evaluations to one or more of these categories and identifying relevant user populations. In addition, building upon the discussion of the different phases of the 3D sketching process, the framework suggests to evaluate relevant sketching tasks, which may range from low-level perception and hand movements to high-level conceptual design. Finally, we discuss limitations and challenges that arise when evaluating 3D sketching, including a lack of standardization of evaluation methods and multiple, potentially conflicting, ways to evaluate the same task and user interface usability; we also identify opportunities for more holistic evaluations. We hope the results can contribute to accelerating research in this domain and, ultimately, broad adoption of immersive sketching systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it