Channel Extraction and Geometric Parameters Measurement Based on Point Clouds
Bibliographic record
Abstract
Abstract. In electrified railways, accurately measuring geometric parameters of pre-embedded tunnel channels is crucial for ensuring the installation precision and operational stability of the railway overhead contact network system. However, traditional manual measurement methods face challenges such as complex construction environments, stringent precision demands, and limited technical capabilities, resulting in inefficiency, significant safety risks, and poor repeatability. To address these issues, this paper introduces a novel framework based on total station point clouds. The framework comprises three key modules: point cloud preprocessing, channel extraction, and geometric parameter measurement. During preprocessing, point clouds are aligned using principal component analysis (PCA), and interference points are removed to enhance data quality. For channel extraction, an Otsu-based curvature threshold is first applied to preliminarily identify channel point clouds. Subsequently, a refined extraction process combining statistical denoising and density-based clustering is employed to isolate the channel point clouds with greater precision. In terms of geometric parameter measurement, an arc-based method is utilized for length measurement, while a generalized Gaussian distribution (GGD)-based approach is adopted for depth estimation. Experimental results demonstrate that the proposed method significantly improves channel extraction performance, achieving an F1-score improvement of up to 24.8%. Furthermore, the framework enables millimeter-level depth estimation with a mean absolute error of 1.90 mm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".