Density Adaptive Plane Segmentation from Long-Range Terrestrial Laser Scanning Data
Bibliographic record
Abstract
Plane segmentation is a commonly used approach that extracts detailed building information from point cloud. However, the change of density is not obvious for mainstream research data. The buildings with different densities are difficult to be classified especially for TLS data of which scanning distance exceeds 500m, while neighborhood radius is the key factor to solve this problem. In this article, an approach for density adaptive plane segmentation is presented. Firstly, compared with methods based on fixed radius range, dynamic neighborhood radius is selected before plane segmentation to ensure that the objects with different densities can be identified and the dimensionality feature of each point can be computed. Then, an improved growing rule based on dimensionality feature is applied to segment the buildings into planes. The experimental results show that the proposed method can efficiently extract planes from long-range TLS data, the precision reaches 95%, the recall reaches 92%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".