3D Highway Curve Reconstruction From Mobile Laser Scanning Point Clouds
Bibliographic record
Abstract
The point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system have shown great potential for many applications such as intelligent transportation systems, road infrastructure inventories, and high-definition (HD) maps to support the advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). This paper presents a novel two-step approach to automated detection and reconstruction of three-dimensional (3D) highway curves from MLS point clouds. However, when dealing with noisy, unstructured, dense point clouds, we often face some challenges, most notably in handling of the outliers introduced during road marking detection and in recognition of curve types during 3D curve reconstruction. Our approach is formed by two main algorithms: a detector based on intensity variance and a robust model fitting estimator. The experimental results obtained using both a virtual scan dataset and a real MLS dataset demonstrated that our approach is very promising in handling of the outliers and reconstruction of 3D road curves. Specifically, a relative accuracy of 0.6% has been achieved in estimation of circle radii based on the virtual scan dataset. A comparative study also showed that our road marking detection approach is more effective and more stable than state-of-the-art approaches.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.001 | 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".