Registration of Aerial Images and LiDAR Point Clouds by Exploiting Global–Local Geometric Constraints of Buildings
Bibliographic record
Abstract
Reliable registration of aerial images and airborne LiDAR point clouds (ALS) is challenging during the integrated three-dimensional reconstruction of the two types of data. This study proposes a novel method for exploiting the global and local geometric constraints of buildings to automatically acquire reliable tie points between aerial images and ALS. First, dense matched point clouds (MPS) are obtained from aerial images. Building instances are extracted from the MPS and ALS using simple filtering and clustering algorithms, respectively. Owing to the similarity of the global geometric distribution of buildings in MPS and ALS, the relationships of building instances are established using a graph matching method. Furthermore, to coarsely align MPS and ALS, line segments are extracted and matched with the local constraints of corresponding building instances. The proposed strategy can overcome the adverse effects of the significant differences between MPS and ALS in terms of density, completeness, and accuracy on the matching results. Moreover, the constraints of corresponding buildings greatly narrow the search range of line segment matching, improving the matching reliability and efficiency. Considering the local geometric deformations in MPS, the iterative closest point algorithm is used for each pair of buildings to realize precise registration. Subsequently, a strategy to select reliable tie points between aerial images and ALS is proposed for improving the accuracy of the orientation parameters of aerial images. The proposed method realizes the accurate registration of aerial images and ALS in two different scenes. The average projection errors of aerial images and ALS on two datasets are as low as 1.16 and 0.89 pixels, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".