Skeleton-Based Registration of 3D Laser Scans for Automated Quality Assurance of Industrial Facilities
Bibliographic record
Abstract
Registration of 3D point clouds is one possible way to compare the as-built and the as-designed status of construction components. Building information models (BIM) contain detailed information about the as-designed state, particularly 3D drawings of construction components. On the other hand, using automated and accurate data acquisition methods such as laser scanning provide reliable and robust information about the as-built status of construction components. Registration therefore makes it possible to automatically compare the designed and built states in order to appropriately plan forward and generate the corrective actions required. This paper presents a new approach for reliably performing the registration with a required level of accuracy and automation within a substantially improved timeframe. Rather than performing the computationally intensive registration methods that may not work robustly for dense point clouds, the proposed framework employs the geometric skeleton of the construction components, which is extremely less dense and therefore computationally less costly for the processing step required. The method is experimentally tested for the components extruded along an axis, such as industrial assemblies (i.e. pipe spools and structural frames) for which a geometric skeleton represents the components abstractly. The registration of 3D point clouds is performed in a computationally less intensive manner, and the framework developed has the potential to be employed for (near) real-time assembly control, quality control and status assessment processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".