Optimization Classification Research Based on Laser Scanning Point Cloud Reflectance Intensity Correction
Bibliographic record
Abstract
The laser intensity information of 3D terrestrial laser scanning point cloud is very important for target classification, but the effect of classification is not ideal because of the influence of reflection material, incident angle and distance. In this paper, the intensity of point cloud is obtained by scanning the experimental wall surface with a 3D terrestrial laser scanning. By using the method of comparative analysis, the normal vector of point cloud is estimated by using k-nearest neighbor points on the wall surface for each area, and the laser scanning incidence angle is calculated. The influence of reflection material, incident angle and distance on laser intensity is analyzed by regression. Through the polynomial regression analysis of scanning distance and laser intensity, the optimized parameters are obtained to correct the laser intensity, and the corrected laser intensity is used to classify the point cloud. The results show that the point cloud laser intensity can be corrected according to the polynomial regression analysis of laser ranging data, and the corrected point cloud laser intensity has a good classification effect.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".