Embroidery modeling and rendering
Bibliographic record
Abstract
Embroidery is a traditional non-photorealistic art form in which threads of different colours stitched into a base material are used to create an image. We explore techniques for automatically producing embroidery layouts from line drawings and for rendering those layouts in real time on potentially deformable 3D objects with hardware acceleration. Layout of stitches is based on automatic extraction of contours from line drawings followed by a set of stitch-placement procedures based on traditional embroidery techniques. Rendering first captures the lighting environment on the surface of the target object and renders the embroidery as an image in texture space. Stitches are rendered in texture space using a lighting model suitable for threads at a resolution that avoids geometric and highlight aliasing, and with alpha-mapped per-stitch boundary antialiasing. Stitches are also rendered in layers to capture the 2.5D nature of embroidery. A filtered texture pyramid is constructed from the resulting texture and applied to the 3D object, using hardware accelerated scale-dependent antialiasing. Aliasing of fine stitch structure and highlights is avoided by this process. The result is a realistic embroidered image that properly responds to lighting in real time.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".