Consistent Point Clouds of Narrow Spaces Using Multiscan Domain Mapping
Bibliographic record
Abstract
Abstract: Three-dimensional (3D) range scanning of large spaces, such as civil structures, generates an immense cloud of 3D points with inconsistent data densities due to the limited positions of the stationary scanner, inaccessible surfaces, and narrow pathways. This density variation is the dominant detrimental factor in extracting accurate scanned shapes. This article introduces an effective scan planning methodology for capturing accurate geometry from long and narrow spaces, which minimizes the need for subsequent data approximations. The technique computes an optimum scanning range for each stationary position of the scanner that limits the density variation to a user-defined value. Three cases are proposed to define the “limited data density” and a FARO®-LS880 laser scanner is used to illustrate the proposed approach that achieves acceptable scanning results in terms of its critical shape capturing capability, overall point cloud density, and accurate point-based visualization. The experimental observations confirm that the accuracy of the scanned data can be improved by registering multiple partial scans with restricted density and positioning the data acquisition device close to the critical features. The latter recommended step decreases the incident angle to the world domain, which, in turn, reduces the surface occlusions and data density variations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".