A PROCEDURE FOR THE REGISTRATION AND SEGMENTATION OF HETEROGENEOUS LIDAR DATA
Bibliographic record
Abstract
Abstract. Laser scanning, whether airborne or terrestrial is being used nowadays for wide spectrum of applications. In addition, many advances have been introduced to the laser scanning technology in the last decade; thus resulting into increased performance in terms of the point density, scanner range, and expected point accuracy. On the other hand, users are encountering scenarios where the integration of various laser datasets becomes essential in order to avoid data gaps (e.g., missing building roofs in the terrestrial scans, or missing structure facades in the airborne case). This problem is usually solved seamlessly through a classical transformation when the average point accuracy is relatively homogeneous over a given dataset. However, this is not usually the case; in this work, we propose a workflow for the optimal registration of multisource point clouds using weighted conformal transformation. First, the individual scans are filtered and the local point attributes are populated through a data characterization step. Then, an ICPP-based weighted registration algorithm is performed over the entire datasets until convergence. Finally, our heterogeneous segmentation procedure is performed in a simultaneous fashion to ensure exploiting the full potential of a dataset. The performance of this algorithm in terms of correctness, automation level, and other factors is evaluated using real datasets with significant variations in point densities and accuracy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".