Developing an optimized texture mapping for photorealistic 3D buildings
Bibliographic record
Abstract
Abstract Texture mapping generates photorealistic representations of three‐dimensional (3D) geometric objects and enhances the spatial perception of areas of interest. Over the past two decades, even though various approaches for 3D urban models have been investigated, their use has been limited because of the lack of spatial accuracy, details, and the complex processes. It is difficult to maintain highly detailed texture information without using a hybrid of aerial image and ground‐based imaging techniques, which are costly. Furthermore, it is hard to develop a fully automated process for 3D urban mapping that achieves high spatial accuracy. With regard to the issues, this research aims to develop a semi‐automated process for 3D building models that would help image‐based approaches. It helps acquire qualified texture information and improve the appearance of building façades in a large city. In particular, this research first investigates an optimal overlap of consecutive aerial images that generates sufficient information to texture each façade, thus making this process more cost‐effective. Second, this research develops an application to semi‐automatically build 3D buildings and textured 3D buildings. The application is developed in C++. The textured 3D building models are quantitatively and qualitatively assessed to determine the usability of the semi‐automated process.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".