Spatial and visual data fusion for capturing, retrieval, and modeling of as-built building geometry and features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 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 it