Automated Registration of 3D Point Clouds with 3D CAD Models for Remote Assessment of Staged Fabrication
Bibliographic record
Abstract
Modularization and preassembly are parts of a trend toward staged fabrication that has been developing in the construction industry in many parts of the world over the past few decades. Successful delivery and transportation of materials in staged fabrication processes always has been a key challenge. Although substantial advances in modularization and prefabrication have been achieved recently, there is still a significant rate of damages and defects occurring during transportation and shipment. In addition, there are inaccuracies in staged-fabricated assemblies because of manually intensive quality control during the fabrication process. Thus, there is a significant need to monitor the fabrication processes continuously to avoid significant rework costs and delays. This paper presents an automated approach to register laser scanned data, which represents as-built status, with 3D CAD models for prefabricated steel assemblies. Moreover, automated registration enhances 3D tolerance analysis for automated quality control of prefabricated assemblies. An iterative closest point (ICP)-based model is used for automated registration in the presented paper. An experimental study is conducted to validate the proposed model for monitoring the fabrication and installation processes. Experimental results show that the presented approach can be used to detect defected parts or fabrication inaccuracies precisely and quickly.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".