On the Digital Reconstruction and Interactive Presentation of Heritage Sites through Time
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
Virtual time travel from existing remains of a heritage site to its previous states and original condition is an educational and interesting experience and can provide better understanding of history. However, digitally reconstructing non-existing objects is a challenge. The interaction and navigation within virtual 4D worlds (adding time to 3D worlds) is also problematical due to the time dimension. In this paper we developed an approach to modelling of heritage sites that has undergone changes over the years. The method creates independent models from different types of data, such as frescos and paintings, drawings, old photos, historic descriptions, and digitization of remains, then assembles and integrates these models for an interactive presentation. Several research issues had to be addressed: (1) Modelling from frescos and drawings with incorrect perspective, (2) modelling from paintings and old photos including fine geometric details from shading (3) colouring models from old photos and drawings to match the colours of existing elements, (4) the seamless and accurate integration of models created independently from different sets of data, and (5) the creation of intuitive interactive presentation that combines all the models and other useful information. We provide contributions to these issues, including our own advanced model viewer, and apply them to modelling of: destroyed Haida house of Chief Weah (Masset, Canada), the demolished and partially relocated Rideau Chapel (Ottawa, Canada), and the Stenico castle (Trentino, Italy) which undergone many changes over several centuries. Each of these diverse examples illustrates different approach for reconstructing heritage sites that changed through time.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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