Comparison of Methods Used for Detecting Unknown Structural Elements in Three-dimensional Point Clouds
Bibliographic record
Abstract
Three-dimensional (3D) imaging technologies, in particular 3D laser scanners, are becoming more accessible and more accurate. These advances are providing engineers and architects with vast quantities of raw, geometric data. Whereas this data is visually appealing and intuitive to the human eye, it contains very little meaning beyond that. The research presented in this paper presents and compares methods for attributing meaning to dense 3D point clouds. Two of the methods developed and presented utilize 2D and 3D Hough transforms to represent the points as lines and planes. The third method uses point segmentation techniques to group points belonging to the same plane. The initial focus is on structural steel systems and connections modeling for analysis of reuse. The advantages and disadvantages of each method are outlined, and each method is evaluated for its potential to provide engineers and architects with useful and meaningful point clouds from 3D laser scanners. The point segmentation techniques exhibit the most potential by allowing for the location and orientation of any surface to be identified.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".