Spatial and visual data fusion for capturing, retrieval, and modeling of as-built building geometry and features
Bibliographic record
Abstract
Abstract Background As-built building information, including building geometry and features, is useful in multiple building assessment and management tasks. However, the current process for capturing, retrieving, and modeling such information is labor-intensive and time-consuming. Methods In order to address these issues, this paper investigates the potentials of fusing visual and spatial data for automatically capturing, retrieving, and modeling as-built building geometry and features. An overall fusion-based framework has been proposed. Under the framework, pairs of 3D point clouds are progressively registered through the RGB-D (Red, Green, Blue plus Depth) mapping. Meanwhile, building elements are recognized based on their visual patterns. The recognition results can be used to label the 3D points, which could facilitate the modeling of building elements. Results So far, two pilot studies have been performed. The results show that a high degree of automation could be achieved for the registration of building scenes captured from different scans and the recognition of building elements with the proposed framework. Conclusions The fusion of spatial and visual data could significantly facilitate the current process of retrieving, modeling, and visualizing as-built information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".