Multiscale point feature object localization for hydrant surveying using LiDAR
Bibliographic record
Abstract
Object localization in outdoor point clouds is essential for urban scene understanding in numerous applications, especially in land surveying. The advent of terrestrial laser scanning (TLS) LiDAR and Deep Learning methods can re-duce the time surveying urban objects in real-world situations. This paper proposes an automatic and effective ob-ject detection and key-point feature detection pipeline for surveying hydrant objects on dense point-cloud scenes. The proposed method consists of two stages. In the first stage, a multiscale voxelization strategy is proposed to reduce the computational load and complexity of dense point clouds, then hierarchical features are extracted to localize the in-terest object. In the second stage, we introduced an auto-matic strategy to seek the hydrant's centroid point using a learning-based method with KD-trees. We exploit the features using PointNet++ and compare the performance under different configurations in both stages. The proposed pipeline demonstrates robustness under challenging sce-narios and represents an intuitive solution for surveying ur-ban objects in dense point-cloud data.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".