Visual Acceptance of Library-Generated CityGML LOD3 Building Models
Bibliographic record
Abstract
The acceptance of 3D building models is critical to all urban 3D visualization projects. Building models that are identified as unacceptable can increase the cost of the project, delay the delivery, and, in some cases, cancel the acceptance of the entire project. A 3D modelling approach of using representative textures and geometry rather than actual photorealistic textures and geometry was conducted to determine whether participants who frequent the building multiple times a week over a period of a year would be able to identify the visual difference. Three focus groups were established and used to evaluate the visual quality of the 3D building models. Participants were asked to rank the visual quality of the building, as well as identifying any geometry, texture, or overall visual quality problems. The participants from the three focus groups did not identify any texture or geometry mistakes present in the building models. The overall visual quality identified by the participants from the three focus groups was above average, suggesting that the 3D modelling approach is an effective means for modelling buildings with high visitation and significance.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".