SEMI-AUTOMATIC CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA USING BUILDINGS
Bibliographic record
Abstract
Abstract. In this work, the co-registration steps between LiDAR and photogrammetric DSM 3Ddata are analyzed and a solution based on automated plane matching is proposed and implemented. For a robust 3D geometric transformation both planes and points are used. Initially planes are chosen as the co-registration primitives. To confine the search space for the plane matching a sequential automatic building matching is performed first. For matching buildings from the LiDAR and the photogrammetric data, a similarity objective function is formed based on the roof height difference (RHD), the 3D histogram of the building attributes, and the building boundary area of a building. A region growing algorithm based on a Triangulated Irregular Network (TIN) is implemented to extract planes from both datasets. Next, an automatic successive process for identifying and matching corresponding planes from the two datasets has been developed and implemented. It is based on the building boundary region and determines plane pairs through a robust matching process thus eliminating outlier pairs. The selected correct plane pairs are the input data for the geometric transformation process. The 3D conformal transformation method in conjunction with the attitude quaternion is applied to obtain the transformation parameters using the normal vectors of the corresponding plane pairs. Following the mapping of one dataset onto the coordinate system of the other, the Iterative Closest Point (ICP) algorithm is then applied, using the corresponding building point clouds to further refine the transformation solution. The results indicate that the combination of planes and points improve the co-registration outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".