Automated Model‐Based Finding of 3D Objects in Cluttered Construction Point Cloud Models
Bibliographic record
Abstract
Finding construction components in cluttered point clouds is a critical pre-processing task that requires intensive and manual operations. Accurate isolation of an object from point clouds is a key for further processing steps such as positive identification, scan-to-building information modeling (BIM), and robotic manipulation. Manual isolaton is tedious, time consuming, and disconnected from the automated tasks involved in the process. This article adapts and examines a method for finding objects within 3D point clouds robustly, quickly, and automatically. A local feature on a pair of points is employed for representing 3D shapes. The method has three steps: (1) offline model library generation, (2) online searching and matching, and (3) match refinement and isolation. Experimental tests are carried out for finding industrial (curvilinear) and structural (rectilinear) elements. The method is verified under various circumstances in order to measure its performance toward addressing the major challenges involved in 3D object finding. Results show that the method is sufficiently quick and robust to be integrated with automated process control frameworks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".