Automatic Point Cloud Registration Using a Single Octagonal Lamp Pole
Bibliographic record
Abstract
Registration is an essential procedure for merging point clouds defined in different coordinate systems associated to different scanner positions and orientations. It is usually the first step before the point clouds are further processed to provide spatial information of a scene to support engineering applications. In this paper, a new automatic registration method based on a novel geometric model of a polygonal object is presented. Since the cross section of the shaft of many lamp poles is octagonal, registration based on an octagonal pyramid model is proposed. The presented method only requires a single, common octagonal lamp pole observed in both point clouds, though actual overlap of the point clouds is not strictly required. It can be performed as long as the model parameters can be estimated by fitting the point observations to the model. Moreover, no user interaction is needed to derive approximate values, so the proposed registration can be completely automated. Three independent datasets captured by two scanners were used to verify the method. The registration accuracies in the horizontal and vertical directions were up to 11.7 mm and 4.4 mm at approximately 62 m and 17 m away from the scanner, respectively. With such high accuracies, the estimated registration parameters can serve as a set of initial parameters for fine registration using algorithm such as the iterative closest point (ICP).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".