AUTOMATIC 3D BUILDING MODEL GENERATION USING A HYBRID APPROACH
Bibliographic record
Abstract
Accurate and up-to-date 3D building models are quite valuable for several applications such as city planning, disaster management, and military simulations. As location-based services and personal navigation become more accessible to the public, automated and efficiently generated 3D models are required more urgently than ever. Considering the importance of 3D building models, they still lack economic and reliable techniques for their generation while taking advantage of the available multi-sensory data from single and multiple platforms. The research conducted on 3D building model generation may fall into the following three categories: data sources used (single or multi-source approaches), the processing strategy (data-driven or model-driven), and the amount of user interaction (semiautomatic or fully automatic). The objectives of this research is to propose fully-automatic building generation approach by integrating data-driven and model-driven methods while making use of multiple images and LiDAR datasets. The focus of reconstruction is on complex structures, which comprise a collection of rectangular primitives. The proposed methodology generates building hypotheses and initial-boundaries from LiDAR data (i.e., data-driven method) and this information is used to restrict the search space and to resolve the matching ambiguities in the images’ space (i.e., model-based image fitting). To reduce the number of involved models, rectangular primitives are used and model parameters, height and slopes are determined from LiDAR data. An automatic algorithm for decomposing the initial LiDAR boundaries into several rectangular primitives is introduced.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".