Road Boundaries Detection Based on Local Normal Saliency From Mobile Laser Scanning Data
Why this work is in the frame
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Bibliographic record
Abstract
The accurate extraction of roads is a prerequisite for the automatic extraction of other road features. This letter describes a method for detecting road boundaries from mobile laser scanning (MLS) point clouds in an urban environment. The key idea of our method is directly constructing a saliency map on 3-D unorganized point clouds to extract road boundaries. The method consists of four major steps, i.e., road partition with the assistance of the vehicle trajectory, salient map construction and salient points extraction, curb detection and curb lowest points extraction, and road boundaries fitting. The performance of the proposed method is evaluated on the point clouds of an urban scene collected by a RIEGL VMX-450 MLS system. The completeness, correctness, and quality of the extracted road boundaries are 95.41%, 99.35%, and 94.81%, respectively. Experimental results demonstrate that our method is feasible for detecting road boundaries in MLS point clouds.
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it