Comparative Approaches of Fast 360-degree Video-Based Survey of Inaccessible Heritage
Bibliographic record
Abstract
Abstract. Fast survey documentation of inaccessible environments often forces surveyors to choose between acquisition speed and geometric accuracy of digital databases. 360-degree panoramas go in the direction of this problem, as they can capture every visible surface in a single video, while the operator's movement through the space remains a variable factor. This paper explores whether changing the trajectory in a straightforward way has a practical impact on the final 3D record. The experiment was conducted in the cloister, church and upper and lower choirs of the abandoned Convent of Nossa Senhora da Saudação in southern Portugal. The instability of the site's structure leaves little tolerance for extensive fieldwork. Experience shows that a short, regular route is sufficient for a general overview, while a more complex route provides a richer description of recessed or highly decorated elements, despite longer processing times. Under artificial or evenly diffused light, the simpler route produces even better results because stable exposure eliminates many of the radiometric breaks that complicate outdoor footage. Therefore, this study provides the first practical recommendations: while path design is important, it can be adapted to suit the scope of the project. This enables 360-degree video-based photogrammetry to be used for tasks ranging from fast condition surveys to more detailed recording.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".