Automatic Detection of Cylindrical Objects in Built Facilities
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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