Autonomous Modeling of Pipes within Point Clouds
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 models are in increasing demand for design, maintenance, operations and construction project management. Point-clouds are the main output of automatic data collection using laser-scanners and photogrammetric technologies. Pipe-works may comprise 50 % of the value of important construction projects such as industrial and research facilities. We have developed a practical and cost-effective approach, based on the Hough-transform and judicious use of domain constraints, to find, recognize and reconstruct/model 3D pipes within point-clouds fully automatically. The developed approach allows pipe growing within a 3D point-cloud. It samples the point-cloud to sequential orthogonal slices, detects pipe’s cross-sections within sampled slices and grows the pipe along the cross sections centerlines. It is validated using laser-scanner data from construction of the Engineering-VI building on the University of Waterloo (UW) campus. The system works on a typical laptop. Recognition and localization results are within a few millimeters. The presented approach opens the gate to broad applications of pipe-network modeling.
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 | 0.000 | 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