Photogrammetric features for the registration of terrestrial laser scans with minimum overlap
Bibliographic record
Abstract
The interest and demand for 3 Dimensional (3D) documentation of man-made structures have increased with the continuous improvement in data acquisition systems and the expanding range of potential applications. Currently, 3D data can be obtained through two technologies: photogrammetry and laser scanning. In spite of the proven quality of static laser scanning, the collection and processing of laser scans is a time consum-ing process and the derivation of complete 3D models requires multiple scans with each scan having its own coordinate system. Therefore, the different scans should be aligned in a common coordinate system. This alignment process is known as “registration”. Current registration techniques require large overlap area between the collected scans in order to obtain reliable estimation of the transformation parameters relating these scans. The main objective of this research is to avoid the requirement of large overlap areas among the laser scans using photogrammetric data for the registration process. Registration methods generally use points as primitives to relate the different laser scans. In this research, photogrammetric linear and planar features are used for the proposed registration method. Finally, qualitative and quantitative quality control measures are proposed for analyzing the result of the proposed registration method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".