Digital Bas-Relief Design: a Novel Shape from Shading-Based Method
Bibliographic record
Abstract
Indexed in SCOPUS. Design of products characterized by high stylistic content and organic shapes in the form of bas-relief (e.g. fashion accessories, commemorative plaques and coins) is traditionally performed starting from handmade drawings or photographs that are manually reproduced by highly skilled craftsmen such as sculptors and engravers and finally digitized by means of 3D scanning. Several Computer-based procedures have been devised with the aim of speeding up this process, which is considerably time consuming, subjective and costly; these are mainly based on image processing techniques such as embossing, enhancement, histogram equalization or dynamic range, also implemented in CAD-based commercial software. However, these approaches are characterized by several limitations preventing them from providing a “correct” final geometry. In view of that, the present work describes a novel method for the creation of digital bas-reliefs from a single image using a Shape From Shading (SFS) based approach with interactive initialization. Image processing-based techniques and minimization SFS methods are first used in order to retrieve a rough version of the objective surface; successively, this is used as initialization for the final reconstruction algorithm. Tested on a set of case studies, the method proved to be effective in providing satisfactory digital bas-relief from single images.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".