Integrated modeling systems for 3D vision
Bibliographic record
Abstract
A joint project between the Interdept. Research Center of the University of Padova (CIRGEO) and Dr. Richard. M. Levy, teaching Professor at the Faculty of Environmental Design of the University of Calgary (Canada), has been undertaken aimed to the generation of a full 3D model of an historical building and the surrounding environment, based on a terrestrial laser scanning survey. The main goal of this project is to provide a 3D representation where two different contents are merged together: the object’s geometry on one hand and a set of related historical and cultural information on the other hand. Indeed, through suitable VR authoring tools, like the Virtools Dev. software, it is now possible to build VR environments around laser scanning-based 3D models, which allow the user with a certain level of interaction with the model itself. This solution opens interesting perspectives towards the use of 3D models as a mean to promote the national Cultural Heritage content among remote people: portable cave systems, comprising of double projector devices, stereo 3D converter and a wide screen display, could be profitable employed to this end. \nIn this paper we report the first results of our VR project, i.e. creating a TLS-based 3D model of the church of Pozzoveggiani, an ancient historical building located 15 km south of Padua (Italy). The second stage of the work, i..e. the generation of an interactive VR environment based on such 3D model is still in progress at current date. Therefore we will focus here on different issues related to the generation of a fully closed model of a complex structure, a situation frequently found in the Cultural Heritage field.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.018 |
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".