Detailed 3D reconstruction of large-scale heritage sites with integrated techniques
Bibliographic record
Abstract
Many cultural heritage applications require 3D reconstruction of real-world objects and scenes. Over the past few years, it has become increasingly common to use 3D digitization and modeling for this purpose. This is mainly due to advances in laser-scanning techniques, 3D modeling software, image-based modeling techniques, computer power, and virtual reality. Our approach integrates several technologies based on our experience over more than a decade of trying to accurately and completely model large-scale heritage monuments and sites. Using both interactive and automatic techniques, we can model a highly detailed structure or site at various levels of detail. We use image-based modeling for basic shape and structural elements, and laser scanning for fine details and sculpted surfaces. To present the site in its proper context, we use image-based rendering for landscapes and surroundings. To apply this approach, we created hundreds of models from sites all over the world for documentation, walk-through movies, and interactive visualization. The results were compelling and encouraging.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".