Metric for Automated Detection and Identification of 3D CAD Elements in 3D Scanned Data
Bibliographic record
Abstract
Being able to efficiently compare as-built against as-planned 3D states is critical for performing efficient building and infrastructure construction, maintenance, and management. Three-dimensional (3D) laser scanners have the potential to be successfully applied to these tasks. Recent commercial products allow the comparison of 3D scanned and 3D CAD data based on CAD forms. Their current use is however limited due to the large amounts of manual data processing required for extracting useful information. By using 3D Computer Aided Design (CAD) models as representations of 3D specifications and Global Positioning System (GPS) technologies, the authors present an approach for automating the comparison of 3D sensed data and 3D CAD data. This new approach does not perform this data comparison based on CAD forms but on point-clouds. This paper discusses the fundamental differences between the two approaches, describes the theoretical implementation of the proposed approach, and presents laboratory experimental results confirming the potential impact of the proposed method on industry’s practices.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".