AUTOMATED RETRIEVAL OF PROJECT THREE-DIMENSIONAL CAD OBJECTS IN RANGE POINT CLOUDS TO SUPPORT AUTOMATED DIMENSIONAL QA/QC
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
SUMMARY: In construction, dimensional quality is critical but is very difficult to achieve, especially with built-in-place elements. As a result, dimensional Quality Assessment / Quality Control (QA/QC) must systematically be conducted, which often delays the value-adding work. Current methods for dimensional QA/QC are labor intensive, time consuming and therefore expensive. Comprehensive dimensional QA/QC approaches are thus often discarded for strategic ones, which may provide misleading dimensional QA/QC results, and result in future rework or failures. In the research presented here, the authors take advantage of new technologies available to the Architectural Engineering Construction & Facility Management industry – 3D Computer-Aided Design (CAD) engines, 3D positioning technologies and 3D laser scanners – to develop a method for automated retrieval of 3D CAD model objects in 3D laser scanner range images. This approach for automated CAD object retrieval allows for the automated and accurate segmentation of the as-built cloud corresponding to each project 3D CAD object, and it is robust with respect to occlusions. The quality of the output data is such that it is possible to use it to perform automated defect detection for dimensional QA/QC. In this paper, the authors first present the developed approach and demonstrate its efficiency through a simple experiment. Then, the authors discuss in more detail how the retrieval output data can be used to support automated dimensional QA/QC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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