DETECTION OF ROAD CURB FROM MOBILE TERRESTRIAL LASER SCANNER POINT CLOUD
Bibliographic record
Abstract
Abstract. The detection of different road furniture such as curb, street floor and sidewalk from point clouds is important in many applications such as road maintenance and city planning. In this paper a pipeline for point cloud processing to detect the road curb from unorganized point clouds captured from a mobile terrestrial laser scanner is proposed. The proposed pipeline utilizes a covariance- based procedure to perform a 3D segmentation of point clouds. Features such as the road curb can be extracted by analyzing the local neighborhood of every point. This is done by computing the surface normal direction and the normalized eigenvalues. These parameters can be used to extract the ground objects, such as curb, street floor and sidewalk. The curb can be isolated from the rest of the ground objects based on the previous parameters in addition to elevation gradient within the local neighborhood. A 2D image processing scheme is also presented to find the curbs as edges in a generated 2D height image. The results show successful detection rates of 78% and 94% using 3D and 2D approaches respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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".