Identification of the Best 3D Viewpoint within the BIM Model: Application to Visual Tasks Related to Facility Management
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
Visualizing building assets within building information modeling (BIM) offers significant opportunities in facility management as it can assist the maintenance and the safety of buildings. Nevertheless, taking decisions based on 3D visualization remains a challenge since the high density of spatial information inside the 3D model requires suitable visualization techniques to achieve the visual task. The occlusion is ubiquitous and, whilst solutions already exist such as transparency, none currently solve this issue with an automatic and suitable management of the camera. In this paper, we propose the first RESTful web application implementing a 3D viewpoint management algorithm and we demonstrate its usability in the visualization of assets based on a BIM model for visual counting in facility management. Via an online questionnaire, empirical tests are conducted with architects, the construction industry, engineers, and surveyors. The results show that a 3D viewpoint that maximizes the visibility of 3D geometric objects inside the viewport significantly improves the success rate, the accuracy, and the certainty of a visual counting task compared to the traditional four side points of view (i.e., from the front, back, left, and right viewpoints). Finally, this first validation lays the foundation of future investigations in the 3D viewpoint usability evaluation, both in terms of visual tasks and application domains.
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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 | 0.001 | 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 | 0.001 |
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 it