Detailed 3D reconstruction of large-scale heritage sites with integrated techniques
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
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.
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.
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 | 0.000 |
| 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