A non-rigid automatic registration method of multi-temporal mobile laser scanning point clouds based on road short marking features
Bibliographic record
Abstract
Abstract Because of the Global Navigation Satellite System (GNSS) signal occlusion, inertial measurement unit drift and other factors on positioning, location deviations of multi-temporal mobile laser scanning (MLS) point clouds collected in the same region are always exist. In order to improve the quality of multi-temporal MLS point clouds, it is necessary to correct the location deviations by point cloud registration. This work presents a non-rigid automatic registration method of multi-temporal MLS laser point clouds based on the characteristics of short road markings. Specifically, the central points at both end edges of short road markings were extracted as control points. The correspondences between control points in different point clouds were obtained by KD-tree and optimized by polygon similarity and Otsu methods. Then, based on the GNSS time and coordinate difference of true correspondences, the mathematical model of non-rigid registration adjustment was constructed by combining with gross error detection and polynomial fitting. Finally, multi-temporal MLS point clouds were registered according to the GNSS time and adjustment results. Validation results demonstrate that the registration accuracy reaches up to 2.8 cm. The proposed method provides a new way for high-precision fusion and change detection of multi-temporal MLS point clouds in road scenes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".