A Fast Registration Method for Building Point Clouds Obtained by Terrestrial Laser Scanner via 2-D Feature Points
Bibliographic record
Abstract
Point cloud registration plays a central role in various applications, such as 3D scene reconstruction, preservation of cultural heritage and deformation monitoring. The point cloud data are usually huge. Processing such huge data is very time-consuming, so a fast and accurate registration method is crucial. However, the existing registration methods still have high computation complexity or low accuracy. To address this issue, we develop a registration method for terrestrial point clouds. The method projects the point clouds onto the horizontal plane. Therefore, our method processes point cloud data in 2D space, leading to high computation efficiency. Then, the 2D feature lines are extracted from the projected point clouds. We calculate the intersection points of the 2D feature lines, which are treated as the 2D feature points. Due to the high accuracy of the 2D feature lines, the 2D feature points also have high accuracy. Thus, our method can get accurate registration results. Afterward, the feature triangles are constructed by using the 2D feature points, and the geometric constraints are utilized to find the corresponding feature triangles for calculating the 2D transformation. This strategy boosts the process of searching for the corresponding 2D feature points. Subsequently, the Z-axis displacement is computed by the cylindrical neighbourhoods. By combining the Z-axis displacement and 2D transformation, the 3D rigid transformation is obtained. Experimental evaluation conducted on two publicly available datasets well demonstrate that the proposed registration method can achieve good computational efficiency and high accuracy. The code will be available at: https://github.com/taowuyong?tab=repositories after publication.
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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 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".