Automatic Detection of Cylindrical Objects in Built Facilities
Bibliographic record
Abstract
Three-dimensional (3D) facility models are in increasing demand for design, maintenance, operations, and construction project management. For industrial and research facilities, a key focus is piping, which may comprise 50% of the value of the facility. In this paper, a practical and cost-effective approach based on the Hough transform and judicious use of domain constraints is presented to automatically find, recognize, and reconstruct 3D pipes within laser-scan-acquired point clouds. The core algorithm utilizes the Hough transform’s efficacy for detecting parametric shapes in noisy data by applying it to projections of orthogonal slices to grow cylindrical pipe shapes within a 3D point-cloud. This supports faster and less-expensive built-facility modeling. It is validated using laser-scanner data from construction of the Engineering-VI building on the University of Waterloo campus. The system works on a typical laptop. Recognition results are within a few millimeters to centimeters accuracy in accordance with the chosen tessellation of the Hough space. Broad applications to pipe-network modeling are possible.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".