MERGING PHOTOGRAMMETRY AND AUGMENTED REALITY: THE CANADIAN LIBRARY OF PARLIAMENT
Bibliographic record
Abstract
Abstract. In recent years, Augmented Reality (AR) technology has experienced considerable progress and the combination of AR and 3D modeling opens up new opportunities regarding 3D data visualization and interaction. Consequently, the dissemination of cultural heritage can benefit from these technologies in order to display the cultural assets as realistically and interactively as possible. In this way, high-accuracy 3D models are integrated in the real world.Nevertheless, progress has also still been limited due to several factors. The paper presents a case study based on the recreation of the Queen Victoria sculpture in an AR application. Furthermore, the environment of the sculpture is simulated by panoramic images, inside the Library of Parliament in Ottawa, Canada. The main problems for the development of an AR smartphone application from panoramic images and photogrammetric 3D data are described in this paper. The characteristics of AR systems are explained in detail, analyzing all the steps involved and the available solutions considered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".